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 gc
  import os
  import tempfile
  import unittest
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
  from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
  from diffusers.models.attention_processor import (
      Attention,
      AttnProcessor,
      AttnProcessor2_0,
      LoRAAttnProcessor,
      LoRAAttnProcessor2_0,
      LoRAXFormersAttnProcessor,
      XFormersAttnProcessor,
  )
  from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       int:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	{}
  			for name in unet.attn_processors.keys():
  						SCREAMING_SNAKE_CASE_			=	None if name.endswith('attn1.processor'   ) else unet.config.cross_attention_dim
  						if name.startswith('mid_block'   ):
  									SCREAMING_SNAKE_CASE_			=	unet.config.block_out_channels[-1]
  						elif name.startswith('up_blocks'   ):
  									SCREAMING_SNAKE_CASE_			=	int(name[len('up_blocks.'   )]   )
  									SCREAMING_SNAKE_CASE_			=	list(reversed(unet.config.block_out_channels   )   )[block_id]
  						elif name.startswith('down_blocks'   ):
  									SCREAMING_SNAKE_CASE_			=	int(name[len('down_blocks.'   )]   )
  									SCREAMING_SNAKE_CASE_			=	unet.config.block_out_channels[block_id]
  						SCREAMING_SNAKE_CASE_			=	(
  						    LoRAAttnProcessor2_0 if hasattr(SCREAMING_SNAKE_CASE					,						'scaled_dot_product_attention'   ) else LoRAAttnProcessor
  						)
  						SCREAMING_SNAKE_CASE_			=	lora_attn_processor_class(
  						    hidden_size=SCREAMING_SNAKE_CASE					,						cross_attention_dim=SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	AttnProcsLayers(SCREAMING_SNAKE_CASE   )
  			return lora_attn_procs, unet_lora_layers
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Optional[Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	{}
  			SCREAMING_SNAKE_CASE_			=	(
  			    LoRAAttnProcessor2_0 if hasattr(SCREAMING_SNAKE_CASE					,						'scaled_dot_product_attention'   ) else LoRAAttnProcessor
  			)
  			for name, module in text_encoder.named_modules():
  						if name.endswith(SCREAMING_SNAKE_CASE   ):
  									SCREAMING_SNAKE_CASE_			=	lora_attn_processor_class(
  									    hidden_size=module.out_proj.out_features					,						cross_attention_dim=SCREAMING_SNAKE_CASE   )
  			return text_lora_attn_procs
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Union[str, Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	AttnProcsLayers(SCREAMING_SNAKE_CASE   )
  			return text_encoder_lora_layers
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE=False   ) ->       Dict:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			for _, attn_proc in text_lora_attn_procs.items():
  						# set up.weights
  						for layer_name, layer_module in attn_proc.named_modules():
  									if layer_name.endswith('_lora'   ):
  												SCREAMING_SNAKE_CASE_			=	(
  												    torch.randn_like(layer_module.up.weight   )
  												    if randn_weight
  												    else torch.zeros_like(layer_module.up.weight   )
  												)
  												SCREAMING_SNAKE_CASE_			=	torch.nn.Parameter(SCREAMING_SNAKE_CASE   )
  
  
  
  class 			a_					(    unittest.TestCase    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				torch.manual_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	UNetaDConditionModel(
  				    block_out_channels=(32, 64)			,    layers_per_block=2			,    sample_size=32			,    in_channels=4			,    out_channels=4			,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,    cross_attention_dim=32			,    )
  				SCREAMING_SNAKE_CASE_			=	DDIMScheduler(
  				    beta_start=0.0_0_0_8_5			,    beta_end=0.0_1_2			,    beta_schedule='scaled_linear'			,    clip_sample=SCREAMING_SNAKE_CASE			,    set_alpha_to_one=SCREAMING_SNAKE_CASE			,    steps_offset=1			,    )
  				torch.manual_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	AutoencoderKL(
  				    block_out_channels=[32, 64]			,    in_channels=3			,    out_channels=3			,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']			,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']			,    latent_channels=4			,    )
  				SCREAMING_SNAKE_CASE_			=	CLIPTextConfig(
  				    bos_token_id=0			,    eos_token_id=2			,    hidden_size=32			,    intermediate_size=37			,    layer_norm_eps=1e-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    pad_token_id=1			,    vocab_size=1000			,    )
  				SCREAMING_SNAKE_CASE_			=	CLIPTextModel(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	create_unet_lora_layers(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_layers(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	{
  				    'unet': unet,
  				    'scheduler': scheduler,
  				    'vae': vae,
  				    'text_encoder': text_encoder,
  				    'tokenizer': tokenizer,
  				    'safety_checker': None,
  				    'feature_extractor': None,
  				}
  				SCREAMING_SNAKE_CASE_			=	{
  				    'unet_lora_layers': unet_lora_layers,
  				    'text_encoder_lora_layers': text_encoder_lora_layers,
  				    'unet_lora_attn_procs': unet_lora_attn_procs,
  				}
  				return pipeline_components, lora_components
  
  
  
  
  
  	def 	A_(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	1
  				SCREAMING_SNAKE_CASE_			=	10
  				SCREAMING_SNAKE_CASE_			=	4
  				SCREAMING_SNAKE_CASE_			=	(32, 32)
  
  				SCREAMING_SNAKE_CASE_			=	torch.manual_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	floats_tensor((batch_size, num_channels) + sizes			)
  				SCREAMING_SNAKE_CASE_			=	torch.randint(1			,    SCREAMING_SNAKE_CASE			,    size=(batch_size, sequence_length)			,    generator=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	{
  				    'prompt': 'A painting of a squirrel eating a burger',
  				    'generator': generator,
  				    'num_inference_steps': 2,
  				    'guidance_scale': 6.0,
  				    'output_type': 'numpy',
  				}
  
  				return noise, input_ids, pipeline_inputs
  
  				# copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb
  
  
  
  
  
  	def 	A_(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	77
  
  				SCREAMING_SNAKE_CASE_			=	torch.randint(2			,    56			,    size=(1, max_seq_length)			,    generator=torch.manual_seed(0			)			)
  
  				SCREAMING_SNAKE_CASE_			=	{}
  				SCREAMING_SNAKE_CASE_			=	inputs
  				return prepared_inputs
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				LoraLoaderMixin.save_lora_weights(
  				    save_directory=SCREAMING_SNAKE_CASE			,    unet_lora_layers=lora_components['unet_lora_layers']			,    text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,    )
  				self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							LoraLoaderMixin.save_lora_weights(
  							    save_directory=SCREAMING_SNAKE_CASE			,    unet_lora_layers=lora_components['unet_lora_layers']			,    text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,    )
  							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							LoraLoaderMixin.save_lora_weights(
  							    save_directory=SCREAMING_SNAKE_CASE			,    unet_lora_layers=lora_components['unet_lora_layers']			,    text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,    safe_serialization=SCREAMING_SNAKE_CASE			,    )
  							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.safetensors'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	lora_components['unet_lora_attn_procs']
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							SCREAMING_SNAKE_CASE_			=	sd_pipe.unet
  							unet.set_attn_processor(SCREAMING_SNAKE_CASE			)
  							unet.save_attn_procs(SCREAMING_SNAKE_CASE			)
  							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	self.get_dummy_tokens()
  
  				# inference without lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_without_lora.shape == (1, 77, 32)
  
  				# create lora_attn_procs with zeroed out up.weights
  				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(pipe.text_encoder			)
  				set_lora_up_weights(SCREAMING_SNAKE_CASE			,    randn_weight=SCREAMING_SNAKE_CASE			)
  
  				# monkey patch
  				pipe._modify_text_encoder(SCREAMING_SNAKE_CASE			)
  
  				# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
  				del text_attn_procs
  				gc.collect()
  
  				# inference with lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_with_lora.shape == (1, 77, 32)
  
  				assert torch.allclose(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"
  
  				# create lora_attn_procs with randn up.weights
  				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(pipe.text_encoder			)
  				set_lora_up_weights(SCREAMING_SNAKE_CASE			,    randn_weight=SCREAMING_SNAKE_CASE			)
  
  				# monkey patch
  				pipe._modify_text_encoder(SCREAMING_SNAKE_CASE			)
  
  				# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
  				del text_attn_procs
  				gc.collect()
  
  				# inference with lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_with_lora.shape == (1, 77, 32)
  
  				assert not torch.allclose(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	self.get_dummy_tokens()
  
  				# inference without lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_without_lora.shape == (1, 77, 32)
  
  				# create lora_attn_procs with randn up.weights
  				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(pipe.text_encoder			)
  				set_lora_up_weights(SCREAMING_SNAKE_CASE			,    randn_weight=SCREAMING_SNAKE_CASE			)
  
  				# monkey patch
  				pipe._modify_text_encoder(SCREAMING_SNAKE_CASE			)
  
  				# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
  				del text_attn_procs
  				gc.collect()
  
  				# inference with lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_with_lora.shape == (1, 77, 32)
  
  				assert not torch.allclose(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "lora outputs should be different to without lora outputs"
  
  				# remove monkey patch
  				pipe._remove_text_encoder_monkey_patch()
  
  				# inference with removed lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_without_lora_removed.shape == (1, 77, 32)
  
  				assert torch.allclose(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "remove lora monkey patch should restore the original outputs"
  
  
  
  
  
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							LoraLoaderMixin.save_lora_weights(
  							    save_directory=SCREAMING_SNAKE_CASE			,    unet_lora_layers=lora_components['unet_lora_layers']			,    text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,    )
  							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			,    cross_attention_kwargs={'scale': 0.5}			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images_with_scale[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(
  				    torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							self.create_lora_weight_file(SCREAMING_SNAKE_CASE			)
  
  							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  							SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  							sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  							# check if vanilla attention processors are used
  							for _, module in sd_pipe.unet.named_modules():
  										if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  													self.assertIsInstance(module.processor			,    (AttnProcessor, AttnProcessor2_0)			)
  
              # load LoRA weight file
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  							# check if lora attention processors are used
  							for _, module in sd_pipe.unet.named_modules():
  										if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  													SCREAMING_SNAKE_CASE_			=	(
  													    LoRAAttnProcessor2_0 if hasattr(SCREAMING_SNAKE_CASE			,    'scaled_dot_product_attention'			) else LoRAAttnProcessor
  													)
  													self.assertIsInstance(module.processor			,    SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@unittest.skipIf(torch_device != 'cuda'			,    'This test is supposed to run on GPU'			)
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							self.create_lora_weight_file(SCREAMING_SNAKE_CASE			)
  
  							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  							SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  							sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  							# enable XFormers
  							sd_pipe.enable_xformers_memory_efficient_attention()
  
  							# check if xFormers attention processors are used
  							for _, module in sd_pipe.unet.named_modules():
  										if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  													self.assertIsInstance(module.processor			,    SCREAMING_SNAKE_CASE			)
  
              # load LoRA weight file
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  							# check if lora attention processors are used
  							for _, module in sd_pipe.unet.named_modules():
  										if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  													self.assertIsInstance(module.processor			,    SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@unittest.skipIf(torch_device != 'cuda'			,    'This test is supposed to run on GPU'			)
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				# enable XFormers
  				sd_pipe.enable_xformers_memory_efficient_attention()
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							LoraLoaderMixin.save_lora_weights(
  							    save_directory=SCREAMING_SNAKE_CASE			,    unet_lora_layers=lora_components['unet_lora_layers']			,    text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,    )
  							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  