# What does this PR do?

This PR adds the UnivNet GAN vocoder model ([paper](https://arxiv.org/pdf/2106.07889.pdf), [code](https://github.com/mindslab-ai/univnet)) to `transformers`, which is the vocoder used in the Tortoise TTS text-to-speech model ([paper](https://arxiv.org/pdf/2305.07243.pdf), [code](https://github.com/neonbjb/tortoise-tts)) which is currently being integrated into `diffusers`. See [this issue](https://github.com/huggingface/diffusers/issues/3891) in `diffusers`.

![univnet_model_architecture](https://github.com/huggingface/transformers/assets/58458699/8a33190a-cd52-4e81-a6ed-2fc921b9f86f)

## Before submitting
- [ ] 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?
- [x] 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.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## 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.

@sanchit-gandhi 
@susnato 

Add UnivNet Vocoder Model for Tortoise TTS Diffusers Integration. # What does this PR do?

This PR adds the UnivNet GAN vocoder model ([paper](https://arxiv.org/pdf/2106.07889.pdf), [code](https://github.com/mindslab-ai/univnet)) to `transformers`, which is the vocoder used in the Tortoise TTS text-to-speech model ([paper](https://arxiv.org/pdf/2305.07243.pdf), [code](https://github.com/neonbjb/tortoise-tts)) which is currently being integrated into `diffusers`. See [this issue](https://github.com/huggingface/diffusers/issues/3891) in `diffusers`.

![univnet_model_architecture](https://github.com/huggingface/transformers/assets/58458699/8a33190a-cd52-4e81-a6ed-2fc921b9f86f)

## Before submitting
- [ ] 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?
- [x] 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.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## 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.

@sanchit-gandhi 
@susnato 

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# PEGASUS-X

## Overview

The PEGASUS-X model was proposed in [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347)  by Jason Phang, Yao Zhao and Peter J. Liu.

PEGASUS-X (PEGASUS eXtended) extends the PEGASUS models for long input summarization through additional long input pretraining and using staggered block-local attention with global tokens in the encoder.

The abstract from the paper is the following:

*While large pretrained Transformer models have proven highly capable at tackling natural language tasks, handling long sequence inputs continues to be a significant challenge. One such task is long input summarization, where inputs are longer than the maximum input context of most pretrained models. Through an extensive set of experiments, we investigate what model architectural changes and pretraining paradigms can most efficiently adapt a pretrained Transformer for long input summarization. We find that a staggered, block-local Transformer with global encoder tokens strikes a good balance of performance and efficiency, and that an additional pretraining phase on long sequences meaningfully improves downstream summarization performance. Based on our findings, we introduce PEGASUS-X, an extension of the PEGASUS model with additional long input pretraining to handle inputs of up to 16K tokens. PEGASUS-X achieves strong performance on long input summarization tasks comparable with much larger models while adding few additional parameters and not requiring model parallelism to train.*

This model was contributed by [zphang](<https://huggingface.co/zphang). The original code can be found [here](https://github.com/google-research/pegasus).

## Documentation resources

- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization)

<Tip>

PEGASUS-X uses the same tokenizer as [PEGASUS](pegasus).

</Tip>

## PegasusXConfig

[[autodoc]] PegasusXConfig

## PegasusXModel

[[autodoc]] PegasusXModel
    - forward

## PegasusXForConditionalGeneration

[[autodoc]] PegasusXForConditionalGeneration
    - forward

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rendered properly in your Markdown viewer.

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# PEGASUS-X

## Overview

The PEGASUS-X model was proposed in [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347)  by Jason Phang, Yao Zhao and Peter J. Liu.

PEGASUS-X (PEGASUS eXtended) extends the PEGASUS models for long input summarization through additional long input pretraining and using staggered block-local attention with global tokens in the encoder.

The abstract from the paper is the following:

*While large pretrained Transformer models have proven highly capable at tackling natural language tasks, handling long sequence inputs continues to be a significant challenge. One such task is long input summarization, where inputs are longer than the maximum input context of most pretrained models. Through an extensive set of experiments, we investigate what model architectural changes and pretraining paradigms can most efficiently adapt a pretrained Transformer for long input summarization. We find that a staggered, block-local Transformer with global encoder tokens strikes a good balance of performance and efficiency, and that an additional pretraining phase on long sequences meaningfully improves downstream summarization performance. Based on our findings, we introduce PEGASUS-X, an extension of the PEGASUS model with additional long input pretraining to handle inputs of up to 16K tokens. PEGASUS-X achieves strong performance on long input summarization tasks comparable with much larger models while adding few additional parameters and not requiring model parallelism to train.*

This model was contributed by [zphang](<https://huggingface.co/zphang). The original code can be found [here](https://github.com/google-research/pegasus).

## Documentation resources

- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization)

<Tip>

PEGASUS-X uses the same tokenizer as [PEGASUS](pegasus).

</Tip>

## PegasusXConfig

[[autodoc]] PegasusXConfig

## PegasusXModel

[[autodoc]] PegasusXModel
    - forward

## PegasusXForConditionalGeneration

[[autodoc]] PegasusXForConditionalGeneration
    - forward
