

from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

lowerCAmelCase_ 			=			TypeVar('''T''', bound=bool)





class    snake_case_     (	Generic[T]      ):







			'''simple docstring'''

			def __init__(  self				:       Tuple							,     _UpperCamelCase				:       T							)   ->None:
									snake_case_     =			value
									snake_case_     =			None
									snake_case_     =			None







			@property
			def        snake_case__(  self				:       Optional[Any]							)   ->T:
									return self._value







			@staticmethod
			def        snake_case__(  _UpperCamelCase				:       RandomizedHeapNode[T] | None							,     _UpperCamelCase				:       RandomizedHeapNode[T] | None							)   ->RandomizedHeapNode[T] | None:
									if not roota:
															return roota

									if not roota:
															return roota

									if roota.value > roota.value:
															snake_case_,					snake_case_     =			roota, roota

									if random.choice([True, False]							):
															snake_case_,					snake_case_     =			roota.right, roota.left

									snake_case_     =			RandomizedHeapNode.merge(roota.left							,     _UpperCamelCase							)

									return roota






class    snake_case_     (	Generic[T]      ):







			'''simple docstring'''

			def __init__(  self				:       Union[str, Any]							,     _UpperCamelCase				:       Iterable[T] | None = ()							)   ->None:
									snake_case_     =			None

									if data:
															for item in data:
																					self.insert(_UpperCamelCase							)







			def        snake_case__(  self				:       Optional[Any]							,     _UpperCamelCase				:       T							)   ->None:
									snake_case_     =			RandomizedHeapNode.merge(self._root							,     RandomizedHeapNode(_UpperCamelCase							)							)







			def        snake_case__(  self				:       Union[str, Any]							)   ->T | None:
									snake_case_     =			self.top()

									if self._root is None:
															return None

									snake_case_     =			RandomizedHeapNode.merge(self._root.left							,     self._root.right							)

									return result







			def        snake_case__(  self				:       List[Any]							)   ->T:
									if not self._root:
															raise IndexError('''Can\'t get top element for the empty heap.'''							)
									return self._root.value







			def        snake_case__(  self				:       List[Any]							)   ->None:
									snake_case_     =			None







			def        snake_case__(  self				:       List[Any]							)   ->list[Any]:
									snake_case_     =			[]
									while self:
															result.append(self.pop()							)

									return result







			def __bool__(  self				:       int							)   ->bool:
									return self._root is not None


if __name__ == "__main__":
					import doctest

					doctest.testmod()


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 			snake_case_     (	unittest.TestCase      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       (JukeboxModel,) if is_torch_available() else ()
			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       "openai/jukebox-1b-lyrics"
			SCREAMING_SNAKE_CASE  :     Union[str, Any]		    =       {
			    "artist": "Zac Brown Band",
			    "genres": "Country",
			    "lyrics": "I met a traveller from an antique land,\n    Who said \"Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ",
			}
			# fmt: off
			SCREAMING_SNAKE_CASE  :     int		    =       [
			    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
			]

			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       [
			    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
			]

			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       [
			    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
			]
			SCREAMING_SNAKE_CASE  :     List[Any]		    =       [
			    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
			]

			SCREAMING_SNAKE_CASE  :     Tuple		    =       [
			    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
			]
			SCREAMING_SNAKE_CASE  :     Union[str, Any]		    =       [
			    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
			]

			SCREAMING_SNAKE_CASE  :     Dict		    =       [1058304, 0, 786432, 7169, 507, 76, 27, 40, 30, 76]

			SCREAMING_SNAKE_CASE  :     str		    =       [
			    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
			]
			SCREAMING_SNAKE_CASE  :     List[Any]		    =       [
			    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
			]
			SCREAMING_SNAKE_CASE  :     Tuple		    =       [
			    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
			]
			SCREAMING_SNAKE_CASE  :     List[str]		    =       [
			    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
			]
			SCREAMING_SNAKE_CASE  :     int		    =       [
			    -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
			]
			SCREAMING_SNAKE_CASE  :     Dict		    =       [
			    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
			]
			SCREAMING_SNAKE_CASE  :     List[str]		    =       [
			    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
			]
			SCREAMING_SNAKE_CASE  :     Any		    =       [
			    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
			]







			def        snake_case__(  self				:       Union[str, Any]							)   ->str:
									snake_case_     =			JukeboxTokenizer.from_pretrained(self.model_id							)
									snake_case_     =			tokenizer(**self.metas							)['''input_ids''']
									return tokens







			@slow
			def        snake_case__(  self				:       int							)   ->str:
									snake_case_     =			JukeboxModel.from_pretrained(self.model_id							,     min_duration=0							).eval()
									snake_case_     =			self.prepare_inputs()

									set_seed(0							)
									snake_case_     =			[torch.zeros(1							,     0							,     dtype=torch.long							).cpu() for _ in range(3							)]
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [0]							,     sample_length=4_0 * model.priors[0].raw_to_tokens							,     save_results=_UpperCamelCase							)
									self.assertIn(zs[0][0].detach().cpu().tolist()							,     [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]							)

									set_seed(0							)
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [1]							,     sample_length=4_0 * model.priors[1].raw_to_tokens							,     save_results=_UpperCamelCase							)
									self.assertIn(zs[1][0].detach().cpu().tolist()							,     [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]							)

									set_seed(0							)
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [2]							,     sample_length=4_0 * model.priors[2].raw_to_tokens							,     save_results=_UpperCamelCase							)
									self.assertIn(zs[2][0].detach().cpu().tolist()							,     [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]							)







			@slow
			def        snake_case__(  self				:       Union[str, Any]							)   ->List[str]:
									snake_case_     =			False
									snake_case_     =			JukeboxModel.from_pretrained(self.model_id							,     min_duration=0							).eval()

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

									snake_case_     =			model.priors[0]
									snake_case_     =			0
									snake_case_     =			top_prior.get_music_tokens_conds(_UpperCamelCase							,     start=_UpperCamelCase							,     end=start + top_prior.n_ctx							)
									snake_case_     =			top_prior.get_metadata(labels[0].clone()							,     _UpperCamelCase							,     1_0_5_8_3_0_4							,     0							)

									self.assertIsNone(_UpperCamelCase							)
									self.assertListEqual(metadata.numpy()[0][:1_0].tolist()							,     self.EXPECTED_Y_COND							)

									snake_case_,					snake_case_,					snake_case_     =			top_prior.get_cond(_UpperCamelCase							,     _UpperCamelCase							)
									torch.testing.assert_allclose(
									    audio_conditioning[0][0][:3_0].detach()							,     torch.tensor(self.EXPECTED_AUDIO_COND							)							,     atol=1e-4							,     rtol=1e-4							)
									torch.testing.assert_allclose(
									    metadata_conditioning[0][0][:3_0].detach()							,     torch.tensor(self.EXPECTED_META_COND							)							,     atol=1e-4							,     rtol=1e-4							)
									torch.testing.assert_allclose(
									    lyric_tokens[0, :3_0].detach()							,     torch.tensor(self.EXPECTED_LYRIC_COND							)							,     atol=1e-4							,     rtol=1e-4							)







			@slow
			def        snake_case__(  self				:       Union[str, Any]							)   ->List[Any]:
									snake_case_     =			False

									snake_case_     =			JukeboxModel.from_pretrained(self.model_id							,     min_duration=0							).eval()
									set_seed(0							)
									snake_case_     =			torch.rand((1, 5_1_2_0, 1)							)
									snake_case_     =			list(self.prepare_inputs()							)

									snake_case_     =			[model.vqvae.encode(_UpperCamelCase							,     start_level=2							,     bs_chunks=waveform.shape[0]							)[0], None, None]
									snake_case_     =			model._sample(
									    _UpperCamelCase							,     _UpperCamelCase							,     sample_levels=[0]							,     save_results=_UpperCamelCase							,     sample_length=4_0 * model.priors[0].raw_to_tokens							)
									torch.testing.assert_allclose(zs[0][0][:4_0]							,     torch.tensor(self.EXPECTED_PRIMED_0							)							)

									snake_case_     =			torch.cat((zs[0], torch.zeros(1							,     2_0_4_8 - zs[0].shape[-1]							))							,     dim=-1							).long()
									snake_case_     =			[upper_a, model.vqvae.encode(_UpperCamelCase							,     start_level=1							,     bs_chunks=waveform.shape[0]							)[0], None]
									snake_case_     =			model._sample(
									    _UpperCamelCase							,     _UpperCamelCase							,     sample_levels=[1]							,     save_results=_UpperCamelCase							,     sample_length=4_0 * model.priors[1].raw_to_tokens							)
									torch.testing.assert_allclose(zs[1][0][:4_0]							,     torch.tensor(self.EXPECTED_PRIMED_1							)							)

									snake_case_     =			torch.cat((zs[1], torch.zeros(1							,     2_0_4_8 - zs[1].shape[-1]							))							,     dim=-1							).long()
									snake_case_     =			[upper_a, upper_a, model.vqvae.encode(_UpperCamelCase							,     start_level=0							,     bs_chunks=waveform.shape[0]							)[0]]
									snake_case_     =			model._sample(
									    _UpperCamelCase							,     _UpperCamelCase							,     sample_levels=[2]							,     save_results=_UpperCamelCase							,     sample_length=4_0 * model.priors[2].raw_to_tokens							)
									torch.testing.assert_allclose(zs[2][0][:4_0].cpu()							,     torch.tensor(self.EXPECTED_PRIMED_2							)							)







			@slow
			def        snake_case__(  self				:       Union[str, Any]							)   ->Optional[Any]:
									snake_case_     =			JukeboxModel.from_pretrained(self.model_id							,     min_duration=0							).eval()
									set_seed(0							)
									snake_case_     =			torch.rand((1, 5_1_2_0, 1)							)
									with torch.no_grad():
															snake_case_     =			model.vqvae.encode(_UpperCamelCase							,     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():
															snake_case_     =			model.vqvae.decode(_UpperCamelCase							,     start_level=2							,     bs_chunks=x.shape[0]							)
									torch.testing.assert_allclose(x[0, :4_0, 0]							,     torch.tensor(self.EXPECTED_VQVAE_DECODE							)							,     atol=1e-4							,     rtol=1e-4							)






@require_torch
class 			snake_case_     (	unittest.TestCase      ):







			'''simple docstring'''

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

			# fmt: off
			SCREAMING_SNAKE_CASE  :     str		    =       [
			    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
			]
			SCREAMING_SNAKE_CASE  :     List[str]		    =       [
			    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
			]

			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       [
			    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
			]
			SCREAMING_SNAKE_CASE  :     Dict		    =       [
			    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
			]

			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       [
			    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
			]
			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       [
			    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
			]

			SCREAMING_SNAKE_CASE  :     Any		    =       [
			    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
			]
			SCREAMING_SNAKE_CASE  :     Optional[Any]		    =       [
			    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
			]

			SCREAMING_SNAKE_CASE  :     List[str]		    =       [
			    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
			]
			SCREAMING_SNAKE_CASE  :     Optional[Any]		    =       [
			    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
			]







			def        snake_case__(  self				:       List[Any]							,     _UpperCamelCase				:       List[str]							)   ->List[Any]:
									snake_case_     =			JukeboxTokenizer.from_pretrained(_UpperCamelCase							)
									snake_case_     =			tokenizer(**self.metas							)['''input_ids''']
									return tokens







			@slow
			def        snake_case__(  self				:       List[Any]							)   ->Any:
									snake_case_     =			JukeboxModel.from_pretrained(self.model_id							,     min_duration=0							).eval()
									snake_case_     =			self.prepare_inputs(self.model_id							)

									set_seed(0							)
									snake_case_     =			[torch.zeros(1							,     0							,     dtype=torch.long							).cpu() for _ in range(3							)]
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [0]							,     sample_length=6_0 * model.priors[0].raw_to_tokens							,     save_results=_UpperCamelCase							)
									self.assertIn(zs[0][0].detach().cpu().tolist()							,     [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]							)

									set_seed(0							)
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [1]							,     sample_length=6_0 * model.priors[1].raw_to_tokens							,     save_results=_UpperCamelCase							)
									self.assertIn(zs[1][0].detach().cpu().tolist()							,     [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]							)

									set_seed(0							)
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [2]							,     sample_length=6_0 * model.priors[2].raw_to_tokens							,     save_results=_UpperCamelCase							)
									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        snake_case__(  self				:       Optional[int]							)   ->List[str]:
									snake_case_     =			JukeboxModel.from_pretrained(self.model_id							,     min_duration=0							).eval()
									snake_case_     =			[i.cuda() for i in self.prepare_inputs(self.model_id							)]

									set_seed(0							)
									model.priors[0].cuda()
									snake_case_     =			[torch.zeros(1							,     0							,     dtype=torch.long							).cuda() for _ in range(3							)]
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [0]							,     sample_length=6_0 * model.priors[0].raw_to_tokens							,     save_results=_UpperCamelCase							)
									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()
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [1]							,     sample_length=6_0 * model.priors[1].raw_to_tokens							,     save_results=_UpperCamelCase							)
									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()
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [2]							,     sample_length=6_0 * model.priors[2].raw_to_tokens							,     save_results=_UpperCamelCase							)
									torch.testing.assert_allclose(zs[2][0].cpu()							,     torch.tensor(self.EXPECTED_GPU_OUTPUTS_0							)							)







			@slow
			def        snake_case__(  self				:       List[Any]							)   ->List[Any]:
									snake_case_     =			'''ArthurZ/jukebox_prior_0'''
									snake_case_     =			JukeboxPrior.from_pretrained(_UpperCamelCase							,     min_duration=0							).eval().half().to('''cuda'''							)

									snake_case_     =			self.prepare_inputs(_UpperCamelCase							)[0].cuda()
									snake_case_     =			model.get_metadata(_UpperCamelCase							,     0							,     7_6_8_0							,     0							)
									set_seed(0							)
									snake_case_     =			model.sample(1							,     metadata=_UpperCamelCase							,     sample_tokens=6_0							)
									self.assertIn(outputs[0].cpu().tolist()							,     [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]							)