# What does this PR do?

This updates the top-level readme files say the project has been tested with Python 3.8+ (instead of 3.7+) and PyTorch 1.10+ (instead of 1.9+). Versions prior to those are no longer supported as of [v4.31.0](https://github.com/huggingface/transformers/releases/tag/v4.31.0). It also updates some other versions in those lists, that had become out of date earlier. The non-English readme files were less up to date than the English readme file `README.md`.

I allowed my editor to remove trailing whitespace, since it does not appear to have been intentional. Rendered Markdown does not appear changed. After editing all seven READMEs, running `make fix-copies` (required to pass CI) propagated this whitespace removal to (just) one of the `index.md` files, which is why that is also changed. However, I understand if whitespace removal may be viewed as best done separately; if requested, I'd be pleased to modify this PR to retain the trailing whitespace.

## Rationale

My reasoning is similar as the reasoning that was given for the previous update to these versions in #24307. As of [**v4.31.0**](https://github.com/huggingface/transformers/releases/tag/v4.31.0), 🤗 Transformers has dropped support for Python 3.7 (#24091) and for PyTorch 1.9 (#24080). Because of that:

- If the version ranges noted in the readme files are not changed, some users are likely to be mislead into expecting new releases of 🤗 Transformers to support those versions.
- A lesser issue is that the claim that this repository is tested on all those versions will gradually become inaccurate as further contributions are made to the repository, now that those versions are not supported.

## Before submitting

- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case.

<!-- I'm sure who, if anyone, I should ping for this. I may edit in a ping later.

## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

Update tested versions in READMEs. # What does this PR do?

This updates the top-level readme files say the project has been tested with Python 3.8+ (instead of 3.7+) and PyTorch 1.10+ (instead of 1.9+). Versions prior to those are no longer supported as of [v4.31.0](https://github.com/huggingface/transformers/releases/tag/v4.31.0). It also updates some other versions in those lists, that had become out of date earlier. The non-English readme files were less up to date than the English readme file `README.md`.

I allowed my editor to remove trailing whitespace, since it does not appear to have been intentional. Rendered Markdown does not appear changed. After editing all seven READMEs, running `make fix-copies` (required to pass CI) propagated this whitespace removal to (just) one of the `index.md` files, which is why that is also changed. However, I understand if whitespace removal may be viewed as best done separately; if requested, I'd be pleased to modify this PR to retain the trailing whitespace.

## Rationale

My reasoning is similar as the reasoning that was given for the previous update to these versions in #24307. As of [**v4.31.0**](https://github.com/huggingface/transformers/releases/tag/v4.31.0), 🤗 Transformers has dropped support for Python 3.7 (#24091) and for PyTorch 1.9 (#24080). Because of that:

- If the version ranges noted in the readme files are not changed, some users are likely to be mislead into expecting new releases of 🤗 Transformers to support those versions.
- A lesser issue is that the claim that this repository is tested on all those versions will gradually become inaccurate as further contributions are made to the repository, now that those versions are not supported.

## Before submitting

- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case.

<!-- I'm sure who, if anyone, I should ping for this. I may edit in a ping later.

## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

<!--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.

⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.

-->

# Neighborhood Attention Transformer

## Overview

NAT was proposed in [Neighborhood Attention Transformer](https://arxiv.org/abs/2204.07143)
by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.

It is a hierarchical vision transformer based on Neighborhood Attention, a sliding-window self attention pattern.

The abstract from the paper is the following:

*We present Neighborhood Attention (NA), the first efficient and scalable sliding-window attention mechanism for vision.
NA is a pixel-wise operation, localizing self attention (SA) to the nearest neighboring pixels, and therefore enjoys a
linear time and space complexity compared to the quadratic complexity of SA. The sliding-window pattern allows NA's
receptive field to grow without needing extra pixel shifts, and preserves translational equivariance, unlike
Swin Transformer's Window Self Attention (WSA). We develop NATTEN (Neighborhood Attention Extension), a Python package
with efficient C++ and CUDA kernels, which allows NA to run up to 40% faster than Swin's WSA while using up to 25% less
memory. We further present Neighborhood Attention Transformer (NAT), a new hierarchical transformer design based on NA
that boosts image classification and downstream vision performance. Experimental results on NAT are competitive;
NAT-Tiny reaches 83.2% top-1 accuracy on ImageNet, 51.4% mAP on MS-COCO and 48.4% mIoU on ADE20K, which is 1.9%
ImageNet accuracy, 1.0% COCO mAP, and 2.6% ADE20K mIoU improvement over a Swin model with similar size. *

Tips:
- One can use the [`AutoImageProcessor`] API to prepare images for the model.
- NAT can be used as a *backbone*. When `output_hidden_states = True`,
it will output both `hidden_states` and `reshaped_hidden_states`.
The `reshaped_hidden_states` have a shape of `(batch, num_channels, height, width)` rather than
`(batch_size, height, width, num_channels)`.

Notes:
- NAT depends on [NATTEN](https://github.com/SHI-Labs/NATTEN/)'s implementation of Neighborhood Attention.
You can install it with pre-built wheels for Linux by referring to [shi-labs.com/natten](https://shi-labs.com/natten),
or build on your system by running `pip install natten`.
Note that the latter will likely take time to compile. NATTEN does not support Windows devices yet.
- Patch size of 4 is only supported at the moment.

<img
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/neighborhood-attention-pattern.jpg"
alt="drawing" width="600"/>

<small> Neighborhood Attention compared to other attention patterns.
Taken from the <a href="https://arxiv.org/abs/2204.07143">original paper</a>.</small>

This model was contributed by [Ali Hassani](https://huggingface.co/alihassanijr).
The original code can be found [here](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer).

## Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with NAT.

<PipelineTag pipeline="image-classification"/>

- [`NatForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
- See also: [Image classification task guide](../tasks/image_classification)

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

## NatConfig

[[autodoc]] NatConfig


## NatModel

[[autodoc]] NatModel
    - forward

## NatForImageClassification

[[autodoc]] NatForImageClassification
    - forward

<!--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.

⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.

-->

# Neighborhood Attention Transformer

## Overview

NAT was proposed in [Neighborhood Attention Transformer](https://arxiv.org/abs/2204.07143)
by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.

It is a hierarchical vision transformer based on Neighborhood Attention, a sliding-window self attention pattern.

The abstract from the paper is the following:

*We present Neighborhood Attention (NA), the first efficient and scalable sliding-window attention mechanism for vision.
NA is a pixel-wise operation, localizing self attention (SA) to the nearest neighboring pixels, and therefore enjoys a
linear time and space complexity compared to the quadratic complexity of SA. The sliding-window pattern allows NA's
receptive field to grow without needing extra pixel shifts, and preserves translational equivariance, unlike
Swin Transformer's Window Self Attention (WSA). We develop NATTEN (Neighborhood Attention Extension), a Python package
with efficient C++ and CUDA kernels, which allows NA to run up to 40% faster than Swin's WSA while using up to 25% less
memory. We further present Neighborhood Attention Transformer (NAT), a new hierarchical transformer design based on NA
that boosts image classification and downstream vision performance. Experimental results on NAT are competitive;
NAT-Tiny reaches 83.2% top-1 accuracy on ImageNet, 51.4% mAP on MS-COCO and 48.4% mIoU on ADE20K, which is 1.9%
ImageNet accuracy, 1.0% COCO mAP, and 2.6% ADE20K mIoU improvement over a Swin model with similar size. *

Tips:
- One can use the [`AutoImageProcessor`] API to prepare images for the model.
- NAT can be used as a *backbone*. When `output_hidden_states = True`,
it will output both `hidden_states` and `reshaped_hidden_states`.
The `reshaped_hidden_states` have a shape of `(batch, num_channels, height, width)` rather than
`(batch_size, height, width, num_channels)`.

Notes:
- NAT depends on [NATTEN](https://github.com/SHI-Labs/NATTEN/)'s implementation of Neighborhood Attention.
You can install it with pre-built wheels for Linux by referring to [shi-labs.com/natten](https://shi-labs.com/natten),
or build on your system by running `pip install natten`.
Note that the latter will likely take time to compile. NATTEN does not support Windows devices yet.
- Patch size of 4 is only supported at the moment.

<img
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/neighborhood-attention-pattern.jpg"
alt="drawing" width="600"/>

<small> Neighborhood Attention compared to other attention patterns.
Taken from the <a href="https://arxiv.org/abs/2204.07143">original paper</a>.</small>

This model was contributed by [Ali Hassani](https://huggingface.co/alihassanijr).
The original code can be found [here](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer).

## Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with NAT.

<PipelineTag pipeline="image-classification"/>

- [`NatForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
- See also: [Image classification task guide](../tasks/image_classification)

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

## NatConfig

[[autodoc]] NatConfig


## NatModel

[[autodoc]] NatModel
    - forward

## NatForImageClassification

[[autodoc]] NatForImageClassification
    - forward
