# 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 

# Copyright 2023 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.

import unittest

from transformers import (
    MODEL_FOR_IMAGE_TO_IMAGE_MAPPING,
    AutoImageProcessor,
    AutoModelForImageToImage,
    ImageToImagePipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
    from PIL import Image

else:

    class Image:
        @staticmethod
        def open(*args, **kwargs):
            pass


@is_pipeline_test
@require_torch
@require_vision
class ImageToImagePipelineTests(unittest.TestCase):
    model_mapping = MODEL_FOR_IMAGE_TO_IMAGE_MAPPING
    examples = [
        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
        "http://images.cocodataset.org/val2017/000000039769.jpg",
    ]

    @require_torch
    @require_vision
    @slow
    def test_pipeline(self):
        model_id = "caidas/swin2SR-classical-sr-x2-64"
        upscaler = pipeline("image-to-image", model=model_id)
        upscaled_list = upscaler(self.examples)

        self.assertEqual(len(upscaled_list), len(self.examples))
        for output in upscaled_list:
            self.assertIsInstance(output, Image.Image)

        self.assertEqual(upscaled_list[0].size, (1296, 976))
        self.assertEqual(upscaled_list[1].size, (1296, 976))

    @require_torch
    @require_vision
    @slow
    def test_pipeline_model_processor(self):
        model_id = "caidas/swin2SR-classical-sr-x2-64"
        model = AutoModelForImageToImage.from_pretrained(model_id)
        image_processor = AutoImageProcessor.from_pretrained(model_id)

        upscaler = ImageToImagePipeline(model=model, image_processor=image_processor)
        upscaled_list = upscaler(self.examples)

        self.assertEqual(len(upscaled_list), len(self.examples))
        for output in upscaled_list:
            self.assertIsInstance(output, Image.Image)

        self.assertEqual(upscaled_list[0].size, (1296, 976))
        self.assertEqual(upscaled_list[1].size, (1296, 976))

# Copyright 2023 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.

import unittest

from transformers import (
    MODEL_FOR_IMAGE_TO_IMAGE_MAPPING,
    AutoImageProcessor,
    AutoModelForImageToImage,
    ImageToImagePipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
    from PIL import Image

else:

    class Image:
        @staticmethod
        def open(*args, **kwargs):
            pass


@is_pipeline_test
@require_torch
@require_vision
class ImageToImagePipelineTests(unittest.TestCase):
    model_mapping = MODEL_FOR_IMAGE_TO_IMAGE_MAPPING
    examples = [
        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
        "http://images.cocodataset.org/val2017/000000039769.jpg",
    ]

    @require_torch
    @require_vision
    @slow
    def test_pipeline(self):
        model_id = "caidas/swin2SR-classical-sr-x2-64"
        upscaler = pipeline("image-to-image", model=model_id)
        upscaled_list = upscaler(self.examples)

        self.assertEqual(len(upscaled_list), len(self.examples))
        for output in upscaled_list:
            self.assertIsInstance(output, Image.Image)

        self.assertEqual(upscaled_list[0].size, (1296, 976))
        self.assertEqual(upscaled_list[1].size, (1296, 976))

    @require_torch
    @require_vision
    @slow
    def test_pipeline_model_processor(self):
        model_id = "caidas/swin2SR-classical-sr-x2-64"
        model = AutoModelForImageToImage.from_pretrained(model_id)
        image_processor = AutoImageProcessor.from_pretrained(model_id)

        upscaler = ImageToImagePipeline(model=model, image_processor=image_processor)
        upscaled_list = upscaler(self.examples)

        self.assertEqual(len(upscaled_list), len(self.examples))
        for output in upscaled_list:
            self.assertIsInstance(output, Image.Image)

        self.assertEqual(upscaled_list[0].size, (1296, 976))
        self.assertEqual(upscaled_list[1].size, (1296, 976))
