This PR aims at integrating Bark, a TTS model, to `transformers`.
`Bark` was designed and trained by [Suno-AI team](https://github.com/suno-ai/bark) and is made of 4 main components:

- A `semantic model ` (also named `text model`), i.e a causal autoregressive transformer (GPT2-like), which takes into input a tokenized text
- A `coarse acoustics model` (also named `coarse model`), also a causal autoregressive transformer, taking into input the results of the last model. It aims at regressing the first two audio codebooks necessary to `encodec`.
- A `fine acoustics model` (`fine model`), this time a non-causal autoencoder transformer, which iteratively predicts the 6 last codebooks based on the sum of the previous codebooks embeddings.
- having predicted 8 codebooks channels of `encodec`, Bark uses `encodec` to generate the output audio array.

Note that each of the first 3 modules can take optional conditional speaker embeddings aiming at conditioning the output audio according to `specific preset voices.
Add bark. This PR aims at integrating Bark, a TTS model, to `transformers`.
`Bark` was designed and trained by [Suno-AI team](https://github.com/suno-ai/bark) and is made of 4 main components:

- A `semantic model ` (also named `text model`), i.e a causal autoregressive transformer (GPT2-like), which takes into input a tokenized text
- A `coarse acoustics model` (also named `coarse model`), also a causal autoregressive transformer, taking into input the results of the last model. It aims at regressing the first two audio codebooks necessary to `encodec`.
- A `fine acoustics model` (`fine model`), this time a non-causal autoencoder transformer, which iteratively predicts the 6 last codebooks based on the sum of the previous codebooks embeddings.
- having predicted 8 codebooks channels of `encodec`, Bark uses `encodec` to generate the output audio array.

Note that each of the first 3 modules can take optional conditional speaker embeddings aiming at conditioning the output audio according to `specific preset voices.
# 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 transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch


class JukeboxTokenizationTest(unittest.TestCase):
    tokenizer_class = JukeboxTokenizer
    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
        """,
    }

    @require_torch
    def test_1b_lyrics_tokenizer(self):
        """
        how to run the same test with openAI
        ...
        """
        import torch

        tokenizer = JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics")
        tokens = tokenizer(**self.metas)["input_ids"]
        # fmt: off
        EXPECTED_OUTPUT = [
            torch.tensor([[
                0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
                76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
                44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
                47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
                76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
                30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 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, 76, 76, 76, 76, 76, 76, 76,
                19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
                76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
                76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
                64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
                30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
                27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
                34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
                27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
                41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
                76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
                44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
                76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
                32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
                40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
                45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
                31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
                45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
                76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
                34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
                31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
                40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
                38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
                78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
                76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
                41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
                27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
                46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
                76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
                41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
                46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
                41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
                78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
                40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
                27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
                76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
                76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
                41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
                76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
                27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
                78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
                34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
                44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
                40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
                78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
                46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
                38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
                40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
                27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
                76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
                76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
                76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
                76, 76]]),
            torch.tensor([[0, 0, 0, 1069, 11]]),
            torch.tensor([[0, 0, 0, 1069, 11]]),
        ]
        # fmt: on
        self.assertTrue(torch.allclose(tokens[0], EXPECTED_OUTPUT[0]))
        self.assertTrue(torch.allclose(tokens[1], EXPECTED_OUTPUT[1]))
        self.assertTrue(torch.allclose(tokens[2], EXPECTED_OUTPUT[2]))

    @require_torch
    def test_5b_lyrics_tokenizer(self):
        """
        The outputs are similar that open AI but do not have the same format as this one is adapted to the HF integration.
        """
        import torch

        tokenizer = JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics")
        tokens = tokenizer(**self.metas)["input_ids"]
        # fmt: off
        EXPECTED_OUTPUT = [
            torch.tensor([[
                0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
                31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
                31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
                40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
                79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
                77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
                27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
                37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
                32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
                77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
                77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
                77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
                46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
                77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
                77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
                77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
                77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
                64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
                40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
                40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
                38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
                31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
                41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
                77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
                46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
                41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
                31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
                31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
                44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
                31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
                38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
                40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
                77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
                27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
                31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
                34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
                31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
                31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
                45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
                31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
                77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
                15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
                11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
                45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
                41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
                44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
                46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
                27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
                77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
                35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
                77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
                31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
                77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
                41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
                77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
                40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
                77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
                77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
                27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
                77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
                77, 77, 77, 77, 77, 77]]),
            torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]),
            torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]),
        ]
        # fmt: on
        self.assertTrue(torch.allclose(tokens[0], EXPECTED_OUTPUT[0]))
        self.assertTrue(torch.allclose(tokens[1], EXPECTED_OUTPUT[1]))
        self.assertTrue(torch.allclose(tokens[2], EXPECTED_OUTPUT[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 transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch


class JukeboxTokenizationTest(unittest.TestCase):
    tokenizer_class = JukeboxTokenizer
    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
        """,
    }

    @require_torch
    def test_1b_lyrics_tokenizer(self):
        """
        how to run the same test with openAI
        ...
        """
        import torch

        tokenizer = JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics")
        tokens = tokenizer(**self.metas)["input_ids"]
        # fmt: off
        EXPECTED_OUTPUT = [
            torch.tensor([[
                0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
                76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
                44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
                47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
                76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
                30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 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, 76, 76, 76, 76, 76, 76, 76,
                19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
                76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
                76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
                64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
                30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
                27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
                34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
                27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
                41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
                76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
                44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
                76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
                32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
                40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
                45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
                31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
                45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
                76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
                34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
                31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
                40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
                38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
                78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
                76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
                41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
                27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
                46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
                76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
                41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
                46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
                41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
                78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
                40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
                27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
                76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
                76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
                41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
                76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
                27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
                78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
                34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
                44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
                40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
                78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
                46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
                38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
                40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
                27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
                76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
                76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
                76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
                76, 76]]),
            torch.tensor([[0, 0, 0, 1069, 11]]),
            torch.tensor([[0, 0, 0, 1069, 11]]),
        ]
        # fmt: on
        self.assertTrue(torch.allclose(tokens[0], EXPECTED_OUTPUT[0]))
        self.assertTrue(torch.allclose(tokens[1], EXPECTED_OUTPUT[1]))
        self.assertTrue(torch.allclose(tokens[2], EXPECTED_OUTPUT[2]))

    @require_torch
    def test_5b_lyrics_tokenizer(self):
        """
        The outputs are similar that open AI but do not have the same format as this one is adapted to the HF integration.
        """
        import torch

        tokenizer = JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics")
        tokens = tokenizer(**self.metas)["input_ids"]
        # fmt: off
        EXPECTED_OUTPUT = [
            torch.tensor([[
                0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
                31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
                31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
                40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
                79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
                77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
                27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
                37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
                32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
                77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
                77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
                77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
                46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
                77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
                77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
                77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
                77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
                64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
                40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
                40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
                38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
                31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
                41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
                77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
                46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
                41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
                31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
                31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
                44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
                31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
                38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
                40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
                77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
                27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
                31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
                34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
                31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
                31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
                45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
                31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
                77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
                15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
                11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
                45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
                41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
                44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
                46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
                27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
                77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
                35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
                77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
                31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
                77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
                41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
                77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
                40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
                77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
                77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
                27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
                77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
                77, 77, 77, 77, 77, 77]]),
            torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]),
            torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]),
        ]
        # fmt: on
        self.assertTrue(torch.allclose(tokens[0], EXPECTED_OUTPUT[0]))
        self.assertTrue(torch.allclose(tokens[1], EXPECTED_OUTPUT[1]))
        self.assertTrue(torch.allclose(tokens[2], EXPECTED_OUTPUT[2]))
