code: 
  
  
  
  
  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 			a_					(    unittest.TestCase    ):
  	A						       =   (JukeboxModel,) if is_torch_available() else ()
  	A						       =   '''openai/jukebox-1b-lyrics'''
  	A						       =   {
  	    '''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
  	A						       =   [
  	    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
  	]
  
  	A						       =   [
  	    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
  	]
  
  	A						       =   [
  	    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
  	]
  	A						       =   [
  	    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
  	]
  
  	A						       =   [
  	    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
  	]
  	A						       =   [
  	    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
  	]
  
  	A						       =   [1058304, 0, 786432, 7169, 507, 76, 27, 40, 30, 76]
  
  	A						       =   [
  	    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
  	]
  	A						       =   [
  	    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
  	]
  	A						       =   [
  	    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
  	]
  	A						       =   [
  	    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
  	]
  	A						       =   [
  	    -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
  	]
  	A						       =   [
  	    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
  	]
  	A						       =   [
  	    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
  	]
  	A						       =   [
  	    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 	A_(       self			)     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	JukeboxTokenizer.from_pretrained(self.model_id			)
  				SCREAMING_SNAKE_CASE_			=	tokenizer(**self.metas			)['input_ids']
  				return tokens
  
  
  
  
  
  	@slow
  	def 	A_(       self			)     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	JukeboxModel.from_pretrained(self.model_id			,    min_duration=0			).eval()
  				SCREAMING_SNAKE_CASE_			=	self.prepare_inputs()
  
  				set_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	[torch.zeros(1			,    0			,    dtype=torch.long			).cpu() for _ in range(3			)]
  				SCREAMING_SNAKE_CASE_			=	model._sample(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    [0]			,    sample_length=40 * model.priors[0].raw_to_tokens			,    save_results=SCREAMING_SNAKE_CASE			)
  				self.assertIn(zs[0][0].detach().cpu().tolist()			,    [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]			)
  
  				set_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	model._sample(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    [1]			,    sample_length=40 * model.priors[1].raw_to_tokens			,    save_results=SCREAMING_SNAKE_CASE			)
  				self.assertIn(zs[1][0].detach().cpu().tolist()			,    [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]			)
  
  				set_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	model._sample(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    [2]			,    sample_length=40 * model.priors[2].raw_to_tokens			,    save_results=SCREAMING_SNAKE_CASE			)
  				self.assertIn(zs[2][0].detach().cpu().tolist()			,    [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]			)
  
  
  
  
  
  	@slow
  	def 	A_(       self			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	False
  				SCREAMING_SNAKE_CASE_			=	JukeboxModel.from_pretrained(self.model_id			,    min_duration=0			).eval()
  
  				SCREAMING_SNAKE_CASE_			=	self.prepare_inputs()
  				set_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	[torch.zeros(1			,    0			,    dtype=torch.long			) for _ in range(3			)]
  
  				SCREAMING_SNAKE_CASE_			=	model.priors[0]
  				SCREAMING_SNAKE_CASE_			=	0
  				SCREAMING_SNAKE_CASE_			=	top_prior.get_music_tokens_conds(SCREAMING_SNAKE_CASE			,    start=SCREAMING_SNAKE_CASE			,    end=start + top_prior.n_ctx			)
  				SCREAMING_SNAKE_CASE_			=	top_prior.get_metadata(labels[0].clone()			,    SCREAMING_SNAKE_CASE			,    105_8304			,    0			)
  
  				self.assertIsNone(SCREAMING_SNAKE_CASE			)
  				self.assertListEqual(metadata.numpy()[0][:10].tolist()			,    self.EXPECTED_Y_COND			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	top_prior.get_cond(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				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 	A_(       self			)     ->				Optional[int]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	False
  
  				SCREAMING_SNAKE_CASE_			=	JukeboxModel.from_pretrained(self.model_id			,    min_duration=0			).eval()
  				set_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	torch.rand((1, 5120, 1)			)
  				SCREAMING_SNAKE_CASE_			=	list(self.prepare_inputs()			)
  
  				SCREAMING_SNAKE_CASE_			=	[model.vqvae.encode(SCREAMING_SNAKE_CASE			,    start_level=2			,    bs_chunks=waveform.shape[0]			)[0], None, None]
  				SCREAMING_SNAKE_CASE_			=	model._sample(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    sample_levels=[0]			,    save_results=SCREAMING_SNAKE_CASE			,    sample_length=40 * model.priors[0].raw_to_tokens			)
  				torch.testing.assert_allclose(zs[0][0][:40]			,    torch.tensor(self.EXPECTED_PRIMED_0			)			)
  
  				SCREAMING_SNAKE_CASE_			=	torch.cat((zs[0], torch.zeros(1			,    2048 - zs[0].shape[-1]			))			,    dim=-1			).long()
  				SCREAMING_SNAKE_CASE_			=	[upper_a, model.vqvae.encode(SCREAMING_SNAKE_CASE			,    start_level=1			,    bs_chunks=waveform.shape[0]			)[0], None]
  				SCREAMING_SNAKE_CASE_			=	model._sample(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    sample_levels=[1]			,    save_results=SCREAMING_SNAKE_CASE			,    sample_length=40 * model.priors[1].raw_to_tokens			)
  				torch.testing.assert_allclose(zs[1][0][:40]			,    torch.tensor(self.EXPECTED_PRIMED_1			)			)
  
  				SCREAMING_SNAKE_CASE_			=	torch.cat((zs[1], torch.zeros(1			,    2048 - zs[1].shape[-1]			))			,    dim=-1			).long()
  				SCREAMING_SNAKE_CASE_			=	[upper_a, upper_a, model.vqvae.encode(SCREAMING_SNAKE_CASE			,    start_level=0			,    bs_chunks=waveform.shape[0]			)[0]]
  				SCREAMING_SNAKE_CASE_			=	model._sample(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    sample_levels=[2]			,    save_results=SCREAMING_SNAKE_CASE			,    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 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	JukeboxModel.from_pretrained(self.model_id			,    min_duration=0			).eval()
  				set_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	torch.rand((1, 5120, 1)			)
  				with torch.no_grad():
  							SCREAMING_SNAKE_CASE_			=	model.vqvae.encode(SCREAMING_SNAKE_CASE			,    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():
  							SCREAMING_SNAKE_CASE_			=	model.vqvae.decode(SCREAMING_SNAKE_CASE			,    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 			a_					(    unittest.TestCase    ):
  	A						       =   (JukeboxModel,) if is_torch_available() else ()
  	A						       =   '''openai/jukebox-5b-lyrics'''
  	A						       =   {
  	    '''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
  	A						       =   [
  	    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
  	]
  	A						       =   [
  	    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
  	]
  
  	A						       =   [
  	    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
  	]
  	A						       =   [
  	    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
  	]
  
  	A						       =   [
  	    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
  	]
  	A						       =   [
  	    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
  	]
  
  	A						       =   [
  	    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
  	]
  	A						       =   [
  	    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
  	]
  
  	A						       =   [
  	    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
  	]
  	A						       =   [
  	    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 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	JukeboxTokenizer.from_pretrained(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	tokenizer(**self.metas			)['input_ids']
  				return tokens
  
  
  
  
  
  	@slow
  	def 	A_(       self			)     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	JukeboxModel.from_pretrained(self.model_id			,    min_duration=0			).eval()
  				SCREAMING_SNAKE_CASE_			=	self.prepare_inputs(self.model_id			)
  
  				set_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	[torch.zeros(1			,    0			,    dtype=torch.long			).cpu() for _ in range(3			)]
  				SCREAMING_SNAKE_CASE_			=	model._sample(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    [0]			,    sample_length=60 * model.priors[0].raw_to_tokens			,    save_results=SCREAMING_SNAKE_CASE			)
  				self.assertIn(zs[0][0].detach().cpu().tolist()			,    [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]			)
  
  				set_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	model._sample(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    [1]			,    sample_length=60 * model.priors[1].raw_to_tokens			,    save_results=SCREAMING_SNAKE_CASE			)
  				self.assertIn(zs[1][0].detach().cpu().tolist()			,    [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]			)
  
  				set_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	model._sample(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    [2]			,    sample_length=60 * model.priors[2].raw_to_tokens			,    save_results=SCREAMING_SNAKE_CASE			)
  				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 	A_(       self			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	JukeboxModel.from_pretrained(self.model_id			,    min_duration=0			).eval()
  				SCREAMING_SNAKE_CASE_			=	[i.cuda() for i in self.prepare_inputs(self.model_id			)]
  
  				set_seed(0			)
  				model.priors[0].cuda()
  				SCREAMING_SNAKE_CASE_			=	[torch.zeros(1			,    0			,    dtype=torch.long			).cuda() for _ in range(3			)]
  				SCREAMING_SNAKE_CASE_			=	model._sample(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    [0]			,    sample_length=60 * model.priors[0].raw_to_tokens			,    save_results=SCREAMING_SNAKE_CASE			)
  				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()
  				SCREAMING_SNAKE_CASE_			=	model._sample(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    [1]			,    sample_length=60 * model.priors[1].raw_to_tokens			,    save_results=SCREAMING_SNAKE_CASE			)
  				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()
  				SCREAMING_SNAKE_CASE_			=	model._sample(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    [2]			,    sample_length=60 * model.priors[2].raw_to_tokens			,    save_results=SCREAMING_SNAKE_CASE			)
  				torch.testing.assert_allclose(zs[2][0].cpu()			,    torch.tensor(self.EXPECTED_GPU_OUTPUTS_0			)			)
  
  
  
  
  
  	@slow
  	def 	A_(       self			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	'ArthurZ/jukebox_prior_0'
  				SCREAMING_SNAKE_CASE_			=	JukeboxPrior.from_pretrained(SCREAMING_SNAKE_CASE			,    min_duration=0			).eval().half().to('cuda'			)
  
  				SCREAMING_SNAKE_CASE_			=	self.prepare_inputs(SCREAMING_SNAKE_CASE			)[0].cuda()
  				SCREAMING_SNAKE_CASE_			=	model.get_metadata(SCREAMING_SNAKE_CASE			,    0			,    7680			,    0			)
  				set_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	model.sample(1			,    metadata=SCREAMING_SNAKE_CASE			,    sample_tokens=60			)
  				self.assertIn(outputs[0].cpu().tolist()			,    [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]			)
  
style_context: 
  
  
  
  
  import os
  import pickle
  import unittest
  from collections import UserDict, namedtuple
  
  import torch
  
  from accelerate.test_utils.testing import require_cuda, require_torch_min_version
  from accelerate.test_utils.training import RegressionModel
  from accelerate.utils import (
      convert_outputs_to_fpaa,
      extract_model_from_parallel,
      find_device,
      listify,
      patch_environment,
      recursively_apply,
      send_to_device,
  )
  
  
  SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					namedtuple("ExampleNamedTuple", "a b c")
  
  
  
  class 			a_					(    unittest.TestCase    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	torch.randn(5			,    2			)
  				SCREAMING_SNAKE_CASE_			=	torch.device('cuda'			) if torch.cuda.is_available() else torch.device('cpu'			)
  
  				SCREAMING_SNAKE_CASE_			=	send_to_device(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta.cpu()			,    SCREAMING_SNAKE_CASE			)			)
  
  				SCREAMING_SNAKE_CASE_			=	send_to_device((tensor, [tensor, tensor], 1)			,    SCREAMING_SNAKE_CASE			)
  				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta[0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertIsInstance(resulta[1]			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta[1][0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertTrue(torch.equal(resulta[1][1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertEqual(resulta[2]			,    1			)
  
  				SCREAMING_SNAKE_CASE_			=	send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}			,    SCREAMING_SNAKE_CASE			)
  				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta['a'].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertIsInstance(resulta['b']			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta['b'][0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertTrue(torch.equal(resulta['b'][1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertEqual(resulta['c']			,    1			)
  
  				SCREAMING_SNAKE_CASE_			=	send_to_device(ExampleNamedTuple(a=SCREAMING_SNAKE_CASE			,    b=[tensor, tensor]			,    c=1			)			,    SCREAMING_SNAKE_CASE			)
  				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta.a.cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertIsInstance(resulta.b			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta.b[0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertTrue(torch.equal(resulta.b[1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertEqual(resulta.c			,    1			)
  
  				SCREAMING_SNAKE_CASE_			=	send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}			)			,    SCREAMING_SNAKE_CASE			)
  				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta['a'].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertIsInstance(resulta['b']			,    SCREAMING_SNAKE_CASE			)
  				self.assertTrue(torch.equal(resulta['b'][0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertTrue(torch.equal(resulta['b'][1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
  				self.assertEqual(resulta['c']			,    1			)
  
  
  
  
  
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				with self.assertRaises(SCREAMING_SNAKE_CASE			) as cm:
  							SCREAMING_SNAKE_CASE_			=	recursively_apply(torch.tensor			,    (torch.tensor(1			), 1)			,    error_on_other_type=SCREAMING_SNAKE_CASE			)
  				self.assertEqual(
  				    str(cm.exception			)			,    'Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'			,    )
  
  
  
  
  
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	torch.tensor([1, 2, 3, 4, 5]			)
  				self.assertEqual(listify(SCREAMING_SNAKE_CASE			)			,    [1, 2, 3, 4, 5]			)
  
  				SCREAMING_SNAKE_CASE_			=	torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]			)
  				self.assertEqual(listify(SCREAMING_SNAKE_CASE			)			,    [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]			)
  
  				SCREAMING_SNAKE_CASE_			=	torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]			)
  				self.assertEqual(
  				    listify(SCREAMING_SNAKE_CASE			)			,    [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				with patch_environment(aa=1			,    BB=2			):
  							self.assertEqual(os.environ.get('AA'			)			,    '1'			)
  							self.assertEqual(os.environ.get('BB'			)			,    '2'			)
  
  				self.assertNotIn('AA'			,    os.environ			)
  				self.assertNotIn('BB'			,    os.environ			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	RegressionModel()
  				SCREAMING_SNAKE_CASE_			=	model.forward
  				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
  				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			,    keep_fpaa_wrapper=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	pickle.dumps(SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@require_cuda
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	RegressionModel()
  				SCREAMING_SNAKE_CASE_			=	model.forward
  				SCREAMING_SNAKE_CASE_			=	torch.cuda.amp.autocast(dtype=torch.floataa			)(model.forward			)
  				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
  				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			,    keep_fpaa_wrapper=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	pickle.dumps(SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@require_cuda
  	@require_torch_min_version(version='2.0'			)
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	RegressionModel()
  				SCREAMING_SNAKE_CASE_			=	model.forward
  				SCREAMING_SNAKE_CASE_			=	torch.cuda.amp.autocast(dtype=torch.floataa			)(model.forward			)
  				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
  				SCREAMING_SNAKE_CASE_			=	torch.compile(model.forward			,    backend='inductor'			)
  				SCREAMING_SNAKE_CASE_			=	torch.randn(4			,    10			).cuda()
  				SCREAMING_SNAKE_CASE_			=	model(SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			)     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	RegressionModel()
  				# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
  				SCREAMING_SNAKE_CASE_			=	torch.nn.parallel.DataParallel(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			)
  
  				self.assertEqual(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@require_torch_min_version(version='2.0'			)
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	RegressionModel()
  				SCREAMING_SNAKE_CASE_			=	torch.compile(SCREAMING_SNAKE_CASE			)
  
  				# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
  				SCREAMING_SNAKE_CASE_			=	torch.nn.parallel.DataParallel(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	torch.compile(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			)
  
  				self.assertEqual(compiled_model._orig_mod			,    compiled_model_unwrapped._orig_mod			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				self.assertEqual(find_device([1, 'a', torch.tensor([1, 2, 3]			)]			)			,    torch.device('cpu'			)			)
  				self.assertEqual(find_device({'a': 1, 'b': torch.tensor([1, 2, 3]			)}			)			,    torch.device('cpu'			)			)
  				self.assertIsNone(find_device([1, 'a']			)			)
  