# What does this PR do?

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<!-- Remove if not applicable -->

Set the `TF32` flag for both PyTorch's CUDA and cuDNN backend.

Currently, the `TrainingArguments` parser only sets the `TF32` flag for the CUDA backend. The user can manually pass `--tf32 False` in the command line, but the `torch.backends.cudnn.allow_tf32` would remain `True` during training. There are also some test cases in that we manually set `torch.backends.cuda.matmul.allow_tf32 = False`.

NOTE: The default value of `torch.backends.cudnn.allow_tf32` for the cuDNN backend is `True` (was added 3 years ago).

## 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?
- [ ] 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.

<!-- 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

 -->

Set `TF32` flag for PyTorch cuDNN backend. # What does this PR do?

<!--
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Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Set the `TF32` flag for both PyTorch's CUDA and cuDNN backend.

Currently, the `TrainingArguments` parser only sets the `TF32` flag for the CUDA backend. The user can manually pass `--tf32 False` in the command line, but the `torch.backends.cudnn.allow_tf32` would remain `True` during training. There are also some test cases in that we manually set `torch.backends.cuda.matmul.allow_tf32 = False`.

NOTE: The default value of `torch.backends.cudnn.allow_tf32` for the cuDNN backend is `True` (was added 3 years ago).

## 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?
- [ ] 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.

<!-- 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

 -->

# coding=utf-8
# 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.
import unittest
from unittest import skip

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow
from transformers.trainer_utils import set_seed


if is_torch_available():
    import torch

    from transformers import JukeboxModel, JukeboxPrior, JukeboxTokenizer


@require_torch
class Jukebox1bModelTester(unittest.TestCase):
    all_model_classes = (JukeboxModel,) if is_torch_available() else ()
    model_id = "openai/jukebox-1b-lyrics"
    metas = {
        "artist": "Zac Brown Band",
        "genres": "Country",
        "lyrics": """I met a traveller from an antique land,
    Who said "Two vast and trunkless legs of stone
    Stand in the desert. . . . Near them, on the sand,
    Half sunk a shattered visage lies, whose frown,
    And wrinkled lip, and sneer of cold command,
    Tell that its sculptor well those passions read
    Which yet survive, stamped on these lifeless things,
    The hand that mocked them, and the heart that fed;
    And on the pedestal, these words appear:
    My name is Ozymandias, King of Kings;
    Look on my Works, ye Mighty, and despair!
    Nothing beside remains. Round the decay
    Of that colossal Wreck, boundless and bare
    The lone and level sands stretch far away
    """,
    }
    # fmt: off
    EXPECTED_OUTPUT_2 = [
        1864, 1536, 1213, 1870, 1357, 1536, 519, 880, 1323, 789, 1082, 534,
        1000, 1445, 1105, 1130, 967, 515, 1434, 1620, 534, 1495, 283, 1445,
        333, 1307, 539, 1631, 1528, 375, 1434, 673, 627, 710, 778, 1883,
        1405, 1276, 1455, 1228
    ]

    EXPECTED_OUTPUT_2_PT_2 = [
        1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653
    ]

    EXPECTED_OUTPUT_1 = [
        1125, 1751, 697, 1776, 1141, 1476, 391, 697, 1125, 684, 867, 416,
        844, 1372, 1274, 717, 1274, 844, 1299, 1419, 697, 1370, 317, 1125,
        191, 1440, 1370, 1440, 1370, 282, 1621, 1370, 368, 349, 867, 1872,
        1262, 869, 1728, 747
    ]
    EXPECTED_OUTPUT_1_PT_2 = [
        416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416
    ]

    EXPECTED_OUTPUT_0 = [
        1755, 842, 307, 1843, 1022, 1395, 234, 1554, 806, 739, 1022, 442,
        616, 556, 268, 1499, 933, 457, 1440, 1837, 755, 985, 308, 902,
        293, 1443, 1671, 1141, 1533, 555, 1562, 1061, 287, 417, 1022, 2008,
        1186, 1015, 1777, 268
    ]
    EXPECTED_OUTPUT_0_PT_2 = [
        854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842,
        185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842,
        591, 842, 1353, 842, 185, 842, 591, 842, 591, 114, 591, 842,
        185, 842, 591, 89
    ]

    EXPECTED_Y_COND = [1058304, 0, 786432, 7169, 507, 76, 27, 40, 30, 76]

    EXPECTED_PRIMED_0 = [
        390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002,
        1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002,
        1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907,
        1907, 1788, 596, 1626
    ]
    EXPECTED_PRIMED_1 = [
        1236, 1668, 1484, 1920, 1848, 1409, 139, 864, 1828, 1272, 1599, 824,
        1672, 139, 555, 1484, 824, 1920, 555, 596, 1579, 1599, 1231, 1599,
        1637, 1407, 212, 824, 1599, 116, 1433, 824, 258, 1599, 1433, 1895,
        1063, 1433, 1433, 1599
    ]
    EXPECTED_PRIMED_2 = [
        1684, 1873, 1119, 1189, 395, 611, 1901, 972, 890, 1337, 1392, 1927,
        96, 972, 672, 780, 1119, 890, 158, 771, 1073, 1927, 353, 1331,
        1269, 1459, 1333, 1645, 812, 1577, 1337, 606, 353, 981, 1466, 619,
        197, 391, 302, 1930
    ]
    EXPECTED_VQVAE_ENCODE = [
        390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002,
        1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002,
        1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907,
        1907, 1788, 596, 1626
    ]
    EXPECTED_VQVAE_DECODE = [
        -0.0492, -0.0524, -0.0565, -0.0640, -0.0686, -0.0684, -0.0677, -0.0664,
        -0.0605, -0.0490, -0.0330, -0.0168, -0.0083, -0.0075, -0.0051, 0.0025,
        0.0136, 0.0261, 0.0386, 0.0497, 0.0580, 0.0599, 0.0583, 0.0614,
        0.0740, 0.0889, 0.1023, 0.1162, 0.1211, 0.1212, 0.1251, 0.1336,
        0.1502, 0.1686, 0.1883, 0.2148, 0.2363, 0.2458, 0.2507, 0.2531
    ]
    EXPECTED_AUDIO_COND = [
        0.0256, -0.0544, 0.1600, -0.0032, 0.1066, 0.0825, -0.0013, 0.3440,
        0.0210, 0.0412, -0.1777, -0.0892, -0.0164, 0.0285, -0.0613, -0.0617,
        -0.0137, -0.0201, -0.0175, 0.0215, -0.0627, 0.0520, -0.0730, 0.0970,
        -0.0100, 0.0442, -0.0586, 0.0207, -0.0015, -0.0082
    ]
    EXPECTED_META_COND = [
        0.0415, 0.0877, 0.0022, -0.0055, 0.0751, 0.0334, 0.0324, -0.0068,
        0.0011, 0.0017, -0.0676, 0.0655, -0.0143, 0.0399, 0.0303, 0.0743,
        -0.0168, -0.0394, -0.1113, 0.0124, 0.0442, 0.0267, -0.0003, -0.1536,
        -0.0116, -0.1837, -0.0180, -0.1026, -0.0777, -0.0456
    ]
    EXPECTED_LYRIC_COND = [
        76, 27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45, 45, 76, 38, 31, 33,
        45, 76, 41, 32, 76, 45, 46, 41, 40, 31, 78, 76
    ]
    # fmt: on

    def prepare_inputs(self):
        tokenizer = JukeboxTokenizer.from_pretrained(self.model_id)
        tokens = tokenizer(**self.metas)["input_ids"]
        return tokens

    @slow
    def test_sampling(self):
        model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()
        labels = self.prepare_inputs()

        set_seed(0)
        zs = [torch.zeros(1, 0, dtype=torch.long).cpu() for _ in range(3)]
        zs = model._sample(zs, labels, [0], sample_length=40 * model.priors[0].raw_to_tokens, save_results=False)
        self.assertIn(zs[0][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2])

        set_seed(0)
        zs = model._sample(zs, labels, [1], sample_length=40 * model.priors[1].raw_to_tokens, save_results=False)
        self.assertIn(zs[1][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2])

        set_seed(0)
        zs = model._sample(zs, labels, [2], sample_length=40 * model.priors[2].raw_to_tokens, save_results=False)
        self.assertIn(zs[2][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2])

    @slow
    def test_conditioning(self):
        torch.backends.cuda.matmul.allow_tf32 = False
        model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()

        labels = self.prepare_inputs()
        set_seed(0)
        zs = [torch.zeros(1, 0, dtype=torch.long) for _ in range(3)]

        top_prior = model.priors[0]
        start = 0
        music_token_conds = top_prior.get_music_tokens_conds(zs, start=start, end=start + top_prior.n_ctx)
        metadata = top_prior.get_metadata(labels[0].clone(), start, 1058304, 0)

        self.assertIsNone(music_token_conds)
        self.assertListEqual(metadata.numpy()[0][:10].tolist(), self.EXPECTED_Y_COND)

        audio_conditioning, metadata_conditioning, lyric_tokens = top_prior.get_cond(music_token_conds, metadata)
        torch.testing.assert_allclose(
            audio_conditioning[0][0][:30].detach(), torch.tensor(self.EXPECTED_AUDIO_COND), atol=1e-4, rtol=1e-4
        )
        torch.testing.assert_allclose(
            metadata_conditioning[0][0][:30].detach(), torch.tensor(self.EXPECTED_META_COND), atol=1e-4, rtol=1e-4
        )
        torch.testing.assert_allclose(
            lyric_tokens[0, :30].detach(), torch.tensor(self.EXPECTED_LYRIC_COND), atol=1e-4, rtol=1e-4
        )

    @slow
    def test_primed_sampling(self):
        torch.backends.cuda.matmul.allow_tf32 = False

        model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()
        set_seed(0)
        waveform = torch.rand((1, 5120, 1))
        tokens = list(self.prepare_inputs())

        zs = [model.vqvae.encode(waveform, start_level=2, bs_chunks=waveform.shape[0])[0], None, None]
        zs = model._sample(
            zs, tokens, sample_levels=[0], save_results=False, sample_length=40 * model.priors[0].raw_to_tokens
        )
        torch.testing.assert_allclose(zs[0][0][:40], torch.tensor(self.EXPECTED_PRIMED_0))

        upper_2 = torch.cat((zs[0], torch.zeros(1, 2048 - zs[0].shape[-1])), dim=-1).long()
        zs = [upper_2, model.vqvae.encode(waveform, start_level=1, bs_chunks=waveform.shape[0])[0], None]
        zs = model._sample(
            zs, tokens, sample_levels=[1], save_results=False, sample_length=40 * model.priors[1].raw_to_tokens
        )
        torch.testing.assert_allclose(zs[1][0][:40], torch.tensor(self.EXPECTED_PRIMED_1))

        upper_1 = torch.cat((zs[1], torch.zeros(1, 2048 - zs[1].shape[-1])), dim=-1).long()
        zs = [upper_2, upper_1, model.vqvae.encode(waveform, start_level=0, bs_chunks=waveform.shape[0])[0]]
        zs = model._sample(
            zs, tokens, sample_levels=[2], save_results=False, sample_length=40 * model.priors[2].raw_to_tokens
        )
        torch.testing.assert_allclose(zs[2][0][:40].cpu(), torch.tensor(self.EXPECTED_PRIMED_2))

    @slow
    def test_vqvae(self):
        model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()
        set_seed(0)
        x = torch.rand((1, 5120, 1))
        with torch.no_grad():
            zs = model.vqvae.encode(x, start_level=2, bs_chunks=x.shape[0])
        torch.testing.assert_allclose(zs[0][0], torch.tensor(self.EXPECTED_VQVAE_ENCODE))

        with torch.no_grad():
            x = model.vqvae.decode(zs, start_level=2, bs_chunks=x.shape[0])
        torch.testing.assert_allclose(x[0, :40, 0], torch.tensor(self.EXPECTED_VQVAE_DECODE), atol=1e-4, rtol=1e-4)


@require_torch
class Jukebox5bModelTester(unittest.TestCase):
    all_model_classes = (JukeboxModel,) if is_torch_available() else ()
    model_id = "openai/jukebox-5b-lyrics"
    metas = {
        "artist": "Zac Brown Band",
        "genres": "Country",
        "lyrics": """I met a traveller from an antique land,
    Who said "Two vast and trunkless legs of stone
    Stand in the desert. . . . Near them, on the sand,
    Half sunk a shattered visage lies, whose frown,
    And wrinkled lip, and sneer of cold command,
    Tell that its sculptor well those passions read
    Which yet survive, stamped on these lifeless things,
    The hand that mocked them, and the heart that fed;
    And on the pedestal, these words appear:
    My name is Ozymandias, King of Kings;
    Look on my Works, ye Mighty, and despair!
    Nothing beside remains. Round the decay
    Of that colossal Wreck, boundless and bare
    The lone and level sands stretch far away
    """,
    }

    # fmt: off
    EXPECTED_OUTPUT_2 = [
        1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        1489, 1489, 1489, 1489, 1150, 1853, 1509, 1150, 1357, 1509, 6, 1272
    ]
    EXPECTED_OUTPUT_2_PT_2 = [
        1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
    ]

    EXPECTED_OUTPUT_1 = [
        1125, 416, 1125, 1125, 1125, 1125, 1125, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
    ]
    EXPECTED_OUTPUT_1_PT_2 = [
        416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416
    ]

    EXPECTED_OUTPUT_0 = [
        1755, 1061, 234, 1755, 1061, 1755, 185, 290, 307, 307, 616, 616,
        616, 616, 616, 616, 307, 290, 417, 1755, 234, 1755, 185, 290,
        290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1755,
        234, 234, 1755, 234, 185, 185, 307, 616, 616, 616, 616, 290,
        1755, 1755, 1755, 234, 234, 1755, 1572, 290, 307, 616, 34, 616
    ]
    EXPECTED_OUTPUT_0_PT_2 = [
        854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842, 185,
        417, 185, 842, 307, 842, 591, 842, 185, 842, 185, 842, 591, 842,
        1353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591,
        89, 591, 842, 591, 842, 591, 417, 1372, 842, 1372, 842, 34, 842,
        185, 89, 591, 842, 185, 842, 591, 632
    ]

    EXPECTED_GPU_OUTPUTS_2 = [
        1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
    ]
    EXPECTED_GPU_OUTPUTS_2_PT_2 = [
        1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 1853, 1177, 1536, 1228,
        710, 475, 1489, 1229, 1224, 231, 1224, 252, 1434, 653, 475,
        1106, 1877, 1599, 1228, 1600, 1683, 1182, 1853, 475, 1864,
        252, 1229, 1434, 2001
    ]

    EXPECTED_GPU_OUTPUTS_1 = [
        1125, 1125, 416, 1125, 1125, 416, 1125, 1125, 416, 416, 1125, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
    ]
    EXPECTED_GPU_OUTPUTS_0 = [
        491, 1755, 34, 1613, 1755, 417, 992, 1613, 222, 842, 1353, 1613,
        844, 632, 185, 1613, 844, 632, 185, 1613, 185, 842, 677, 1613,
        185, 114, 1353, 1613, 307, 89, 844, 1613, 307, 1332, 234, 1979,
        307, 89, 1353, 616, 34, 842, 185, 842, 34, 842, 185, 842,
        307, 114, 185, 89, 34, 1268, 185, 89, 34, 842, 185, 89
    ]
    # fmt: on

    def prepare_inputs(self, model_id):
        tokenizer = JukeboxTokenizer.from_pretrained(model_id)
        tokens = tokenizer(**self.metas)["input_ids"]
        return tokens

    @slow
    def test_sampling(self):
        model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()
        labels = self.prepare_inputs(self.model_id)

        set_seed(0)
        zs = [torch.zeros(1, 0, dtype=torch.long).cpu() for _ in range(3)]
        zs = model._sample(zs, labels, [0], sample_length=60 * model.priors[0].raw_to_tokens, save_results=False)
        self.assertIn(zs[0][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2])

        set_seed(0)
        zs = model._sample(zs, labels, [1], sample_length=60 * model.priors[1].raw_to_tokens, save_results=False)
        self.assertIn(zs[1][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2])

        set_seed(0)
        zs = model._sample(zs, labels, [2], sample_length=60 * model.priors[2].raw_to_tokens, save_results=False)
        self.assertIn(zs[2][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2])

    @slow
    @skip("Not enough GPU memory on CI runners")
    def test_slow_sampling(self):
        model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()
        labels = [i.cuda() for i in self.prepare_inputs(self.model_id)]

        set_seed(0)
        model.priors[0].cuda()
        zs = [torch.zeros(1, 0, dtype=torch.long).cuda() for _ in range(3)]
        zs = model._sample(zs, labels, [0], sample_length=60 * model.priors[0].raw_to_tokens, save_results=False)
        torch.testing.assert_allclose(zs[0][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_2))
        model.priors[0].cpu()

        set_seed(0)
        model.priors[1].cuda()
        zs = model._sample(zs, labels, [1], sample_length=60 * model.priors[1].raw_to_tokens, save_results=False)
        torch.testing.assert_allclose(zs[1][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_1))
        model.priors[1].cpu()

        set_seed(0)
        model.priors[2].cuda()
        zs = model._sample(zs, labels, [2], sample_length=60 * model.priors[2].raw_to_tokens, save_results=False)
        torch.testing.assert_allclose(zs[2][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_0))

    @slow
    def test_fp16_slow_sampling(self):
        prior_id = "ArthurZ/jukebox_prior_0"
        model = JukeboxPrior.from_pretrained(prior_id, min_duration=0).eval().half().to("cuda")

        labels = self.prepare_inputs(prior_id)[0].cuda()
        metadata = model.get_metadata(labels, 0, 7680, 0)
        set_seed(0)
        outputs = model.sample(1, metadata=metadata, sample_tokens=60)
        self.assertIn(outputs[0].cpu().tolist(), [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2])

# coding=utf-8
# 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.
import unittest
from unittest import skip

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow
from transformers.trainer_utils import set_seed


if is_torch_available():
    import torch

    from transformers import JukeboxModel, JukeboxPrior, JukeboxTokenizer


@require_torch
class Jukebox1bModelTester(unittest.TestCase):
    all_model_classes = (JukeboxModel,) if is_torch_available() else ()
    model_id = "openai/jukebox-1b-lyrics"
    metas = {
        "artist": "Zac Brown Band",
        "genres": "Country",
        "lyrics": """I met a traveller from an antique land,
    Who said "Two vast and trunkless legs of stone
    Stand in the desert. . . . Near them, on the sand,
    Half sunk a shattered visage lies, whose frown,
    And wrinkled lip, and sneer of cold command,
    Tell that its sculptor well those passions read
    Which yet survive, stamped on these lifeless things,
    The hand that mocked them, and the heart that fed;
    And on the pedestal, these words appear:
    My name is Ozymandias, King of Kings;
    Look on my Works, ye Mighty, and despair!
    Nothing beside remains. Round the decay
    Of that colossal Wreck, boundless and bare
    The lone and level sands stretch far away
    """,
    }
    # fmt: off
    EXPECTED_OUTPUT_2 = [
        1864, 1536, 1213, 1870, 1357, 1536, 519, 880, 1323, 789, 1082, 534,
        1000, 1445, 1105, 1130, 967, 515, 1434, 1620, 534, 1495, 283, 1445,
        333, 1307, 539, 1631, 1528, 375, 1434, 673, 627, 710, 778, 1883,
        1405, 1276, 1455, 1228
    ]

    EXPECTED_OUTPUT_2_PT_2 = [
        1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653
    ]

    EXPECTED_OUTPUT_1 = [
        1125, 1751, 697, 1776, 1141, 1476, 391, 697, 1125, 684, 867, 416,
        844, 1372, 1274, 717, 1274, 844, 1299, 1419, 697, 1370, 317, 1125,
        191, 1440, 1370, 1440, 1370, 282, 1621, 1370, 368, 349, 867, 1872,
        1262, 869, 1728, 747
    ]
    EXPECTED_OUTPUT_1_PT_2 = [
        416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416
    ]

    EXPECTED_OUTPUT_0 = [
        1755, 842, 307, 1843, 1022, 1395, 234, 1554, 806, 739, 1022, 442,
        616, 556, 268, 1499, 933, 457, 1440, 1837, 755, 985, 308, 902,
        293, 1443, 1671, 1141, 1533, 555, 1562, 1061, 287, 417, 1022, 2008,
        1186, 1015, 1777, 268
    ]
    EXPECTED_OUTPUT_0_PT_2 = [
        854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842,
        185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842,
        591, 842, 1353, 842, 185, 842, 591, 842, 591, 114, 591, 842,
        185, 842, 591, 89
    ]

    EXPECTED_Y_COND = [1058304, 0, 786432, 7169, 507, 76, 27, 40, 30, 76]

    EXPECTED_PRIMED_0 = [
        390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002,
        1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002,
        1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907,
        1907, 1788, 596, 1626
    ]
    EXPECTED_PRIMED_1 = [
        1236, 1668, 1484, 1920, 1848, 1409, 139, 864, 1828, 1272, 1599, 824,
        1672, 139, 555, 1484, 824, 1920, 555, 596, 1579, 1599, 1231, 1599,
        1637, 1407, 212, 824, 1599, 116, 1433, 824, 258, 1599, 1433, 1895,
        1063, 1433, 1433, 1599
    ]
    EXPECTED_PRIMED_2 = [
        1684, 1873, 1119, 1189, 395, 611, 1901, 972, 890, 1337, 1392, 1927,
        96, 972, 672, 780, 1119, 890, 158, 771, 1073, 1927, 353, 1331,
        1269, 1459, 1333, 1645, 812, 1577, 1337, 606, 353, 981, 1466, 619,
        197, 391, 302, 1930
    ]
    EXPECTED_VQVAE_ENCODE = [
        390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002,
        1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002,
        1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907,
        1907, 1788, 596, 1626
    ]
    EXPECTED_VQVAE_DECODE = [
        -0.0492, -0.0524, -0.0565, -0.0640, -0.0686, -0.0684, -0.0677, -0.0664,
        -0.0605, -0.0490, -0.0330, -0.0168, -0.0083, -0.0075, -0.0051, 0.0025,
        0.0136, 0.0261, 0.0386, 0.0497, 0.0580, 0.0599, 0.0583, 0.0614,
        0.0740, 0.0889, 0.1023, 0.1162, 0.1211, 0.1212, 0.1251, 0.1336,
        0.1502, 0.1686, 0.1883, 0.2148, 0.2363, 0.2458, 0.2507, 0.2531
    ]
    EXPECTED_AUDIO_COND = [
        0.0256, -0.0544, 0.1600, -0.0032, 0.1066, 0.0825, -0.0013, 0.3440,
        0.0210, 0.0412, -0.1777, -0.0892, -0.0164, 0.0285, -0.0613, -0.0617,
        -0.0137, -0.0201, -0.0175, 0.0215, -0.0627, 0.0520, -0.0730, 0.0970,
        -0.0100, 0.0442, -0.0586, 0.0207, -0.0015, -0.0082
    ]
    EXPECTED_META_COND = [
        0.0415, 0.0877, 0.0022, -0.0055, 0.0751, 0.0334, 0.0324, -0.0068,
        0.0011, 0.0017, -0.0676, 0.0655, -0.0143, 0.0399, 0.0303, 0.0743,
        -0.0168, -0.0394, -0.1113, 0.0124, 0.0442, 0.0267, -0.0003, -0.1536,
        -0.0116, -0.1837, -0.0180, -0.1026, -0.0777, -0.0456
    ]
    EXPECTED_LYRIC_COND = [
        76, 27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45, 45, 76, 38, 31, 33,
        45, 76, 41, 32, 76, 45, 46, 41, 40, 31, 78, 76
    ]
    # fmt: on

    def prepare_inputs(self):
        tokenizer = JukeboxTokenizer.from_pretrained(self.model_id)
        tokens = tokenizer(**self.metas)["input_ids"]
        return tokens

    @slow
    def test_sampling(self):
        model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()
        labels = self.prepare_inputs()

        set_seed(0)
        zs = [torch.zeros(1, 0, dtype=torch.long).cpu() for _ in range(3)]
        zs = model._sample(zs, labels, [0], sample_length=40 * model.priors[0].raw_to_tokens, save_results=False)
        self.assertIn(zs[0][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2])

        set_seed(0)
        zs = model._sample(zs, labels, [1], sample_length=40 * model.priors[1].raw_to_tokens, save_results=False)
        self.assertIn(zs[1][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2])

        set_seed(0)
        zs = model._sample(zs, labels, [2], sample_length=40 * model.priors[2].raw_to_tokens, save_results=False)
        self.assertIn(zs[2][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2])

    @slow
    def test_conditioning(self):
        torch.backends.cuda.matmul.allow_tf32 = False
        torch.backends.cudnn.allow_tf32 = False
        model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()

        labels = self.prepare_inputs()
        set_seed(0)
        zs = [torch.zeros(1, 0, dtype=torch.long) for _ in range(3)]

        top_prior = model.priors[0]
        start = 0
        music_token_conds = top_prior.get_music_tokens_conds(zs, start=start, end=start + top_prior.n_ctx)
        metadata = top_prior.get_metadata(labels[0].clone(), start, 1058304, 0)

        self.assertIsNone(music_token_conds)
        self.assertListEqual(metadata.numpy()[0][:10].tolist(), self.EXPECTED_Y_COND)

        audio_conditioning, metadata_conditioning, lyric_tokens = top_prior.get_cond(music_token_conds, metadata)
        torch.testing.assert_allclose(
            audio_conditioning[0][0][:30].detach(), torch.tensor(self.EXPECTED_AUDIO_COND), atol=1e-4, rtol=1e-4
        )
        torch.testing.assert_allclose(
            metadata_conditioning[0][0][:30].detach(), torch.tensor(self.EXPECTED_META_COND), atol=1e-4, rtol=1e-4
        )
        torch.testing.assert_allclose(
            lyric_tokens[0, :30].detach(), torch.tensor(self.EXPECTED_LYRIC_COND), atol=1e-4, rtol=1e-4
        )

    @slow
    def test_primed_sampling(self):
        torch.backends.cuda.matmul.allow_tf32 = False
        torch.backends.cudnn.allow_tf32 = False

        model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()
        set_seed(0)
        waveform = torch.rand((1, 5120, 1))
        tokens = list(self.prepare_inputs())

        zs = [model.vqvae.encode(waveform, start_level=2, bs_chunks=waveform.shape[0])[0], None, None]
        zs = model._sample(
            zs, tokens, sample_levels=[0], save_results=False, sample_length=40 * model.priors[0].raw_to_tokens
        )
        torch.testing.assert_allclose(zs[0][0][:40], torch.tensor(self.EXPECTED_PRIMED_0))

        upper_2 = torch.cat((zs[0], torch.zeros(1, 2048 - zs[0].shape[-1])), dim=-1).long()
        zs = [upper_2, model.vqvae.encode(waveform, start_level=1, bs_chunks=waveform.shape[0])[0], None]
        zs = model._sample(
            zs, tokens, sample_levels=[1], save_results=False, sample_length=40 * model.priors[1].raw_to_tokens
        )
        torch.testing.assert_allclose(zs[1][0][:40], torch.tensor(self.EXPECTED_PRIMED_1))

        upper_1 = torch.cat((zs[1], torch.zeros(1, 2048 - zs[1].shape[-1])), dim=-1).long()
        zs = [upper_2, upper_1, model.vqvae.encode(waveform, start_level=0, bs_chunks=waveform.shape[0])[0]]
        zs = model._sample(
            zs, tokens, sample_levels=[2], save_results=False, sample_length=40 * model.priors[2].raw_to_tokens
        )
        torch.testing.assert_allclose(zs[2][0][:40].cpu(), torch.tensor(self.EXPECTED_PRIMED_2))

    @slow
    def test_vqvae(self):
        model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()
        set_seed(0)
        x = torch.rand((1, 5120, 1))
        with torch.no_grad():
            zs = model.vqvae.encode(x, start_level=2, bs_chunks=x.shape[0])
        torch.testing.assert_allclose(zs[0][0], torch.tensor(self.EXPECTED_VQVAE_ENCODE))

        with torch.no_grad():
            x = model.vqvae.decode(zs, start_level=2, bs_chunks=x.shape[0])
        torch.testing.assert_allclose(x[0, :40, 0], torch.tensor(self.EXPECTED_VQVAE_DECODE), atol=1e-4, rtol=1e-4)


@require_torch
class Jukebox5bModelTester(unittest.TestCase):
    all_model_classes = (JukeboxModel,) if is_torch_available() else ()
    model_id = "openai/jukebox-5b-lyrics"
    metas = {
        "artist": "Zac Brown Band",
        "genres": "Country",
        "lyrics": """I met a traveller from an antique land,
    Who said "Two vast and trunkless legs of stone
    Stand in the desert. . . . Near them, on the sand,
    Half sunk a shattered visage lies, whose frown,
    And wrinkled lip, and sneer of cold command,
    Tell that its sculptor well those passions read
    Which yet survive, stamped on these lifeless things,
    The hand that mocked them, and the heart that fed;
    And on the pedestal, these words appear:
    My name is Ozymandias, King of Kings;
    Look on my Works, ye Mighty, and despair!
    Nothing beside remains. Round the decay
    Of that colossal Wreck, boundless and bare
    The lone and level sands stretch far away
    """,
    }

    # fmt: off
    EXPECTED_OUTPUT_2 = [
        1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        1489, 1489, 1489, 1489, 1150, 1853, 1509, 1150, 1357, 1509, 6, 1272
    ]
    EXPECTED_OUTPUT_2_PT_2 = [
        1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
    ]

    EXPECTED_OUTPUT_1 = [
        1125, 416, 1125, 1125, 1125, 1125, 1125, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
    ]
    EXPECTED_OUTPUT_1_PT_2 = [
        416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416
    ]

    EXPECTED_OUTPUT_0 = [
        1755, 1061, 234, 1755, 1061, 1755, 185, 290, 307, 307, 616, 616,
        616, 616, 616, 616, 307, 290, 417, 1755, 234, 1755, 185, 290,
        290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1755,
        234, 234, 1755, 234, 185, 185, 307, 616, 616, 616, 616, 290,
        1755, 1755, 1755, 234, 234, 1755, 1572, 290, 307, 616, 34, 616
    ]
    EXPECTED_OUTPUT_0_PT_2 = [
        854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842, 185,
        417, 185, 842, 307, 842, 591, 842, 185, 842, 185, 842, 591, 842,
        1353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591,
        89, 591, 842, 591, 842, 591, 417, 1372, 842, 1372, 842, 34, 842,
        185, 89, 591, 842, 185, 842, 591, 632
    ]

    EXPECTED_GPU_OUTPUTS_2 = [
        1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
    ]
    EXPECTED_GPU_OUTPUTS_2_PT_2 = [
        1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
        653, 653, 653, 653, 653, 653, 653, 1853, 1177, 1536, 1228,
        710, 475, 1489, 1229, 1224, 231, 1224, 252, 1434, 653, 475,
        1106, 1877, 1599, 1228, 1600, 1683, 1182, 1853, 475, 1864,
        252, 1229, 1434, 2001
    ]

    EXPECTED_GPU_OUTPUTS_1 = [
        1125, 1125, 416, 1125, 1125, 416, 1125, 1125, 416, 416, 1125, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
        416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
    ]
    EXPECTED_GPU_OUTPUTS_0 = [
        491, 1755, 34, 1613, 1755, 417, 992, 1613, 222, 842, 1353, 1613,
        844, 632, 185, 1613, 844, 632, 185, 1613, 185, 842, 677, 1613,
        185, 114, 1353, 1613, 307, 89, 844, 1613, 307, 1332, 234, 1979,
        307, 89, 1353, 616, 34, 842, 185, 842, 34, 842, 185, 842,
        307, 114, 185, 89, 34, 1268, 185, 89, 34, 842, 185, 89
    ]
    # fmt: on

    def prepare_inputs(self, model_id):
        tokenizer = JukeboxTokenizer.from_pretrained(model_id)
        tokens = tokenizer(**self.metas)["input_ids"]
        return tokens

    @slow
    def test_sampling(self):
        model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()
        labels = self.prepare_inputs(self.model_id)

        set_seed(0)
        zs = [torch.zeros(1, 0, dtype=torch.long).cpu() for _ in range(3)]
        zs = model._sample(zs, labels, [0], sample_length=60 * model.priors[0].raw_to_tokens, save_results=False)
        self.assertIn(zs[0][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2])

        set_seed(0)
        zs = model._sample(zs, labels, [1], sample_length=60 * model.priors[1].raw_to_tokens, save_results=False)
        self.assertIn(zs[1][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2])

        set_seed(0)
        zs = model._sample(zs, labels, [2], sample_length=60 * model.priors[2].raw_to_tokens, save_results=False)
        self.assertIn(zs[2][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2])

    @slow
    @skip("Not enough GPU memory on CI runners")
    def test_slow_sampling(self):
        model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()
        labels = [i.cuda() for i in self.prepare_inputs(self.model_id)]

        set_seed(0)
        model.priors[0].cuda()
        zs = [torch.zeros(1, 0, dtype=torch.long).cuda() for _ in range(3)]
        zs = model._sample(zs, labels, [0], sample_length=60 * model.priors[0].raw_to_tokens, save_results=False)
        torch.testing.assert_allclose(zs[0][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_2))
        model.priors[0].cpu()

        set_seed(0)
        model.priors[1].cuda()
        zs = model._sample(zs, labels, [1], sample_length=60 * model.priors[1].raw_to_tokens, save_results=False)
        torch.testing.assert_allclose(zs[1][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_1))
        model.priors[1].cpu()

        set_seed(0)
        model.priors[2].cuda()
        zs = model._sample(zs, labels, [2], sample_length=60 * model.priors[2].raw_to_tokens, save_results=False)
        torch.testing.assert_allclose(zs[2][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_0))

    @slow
    def test_fp16_slow_sampling(self):
        prior_id = "ArthurZ/jukebox_prior_0"
        model = JukeboxPrior.from_pretrained(prior_id, min_duration=0).eval().half().to("cuda")

        labels = self.prepare_inputs(prior_id)[0].cuda()
        metadata = model.get_metadata(labels, 0, 7680, 0)
        set_seed(0)
        outputs = model.sample(1, metadata=metadata, sample_tokens=60)
        self.assertIn(outputs[0].cpu().tolist(), [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2])
