

lowerCAmelCase_ 			=			(1, 1_53, 3_70, 3_71, 16_34, 24_67_80_51, 1_15_13_22_19_01_87_63_99_25_65_09_55_97_97_39_71_52_24_01)
lowerCAmelCase_ 			=			(-1_53, -1, 0, 1.2, 2_00, "A", [], {}, None)


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						if not isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ) or n < 1:
												return False

						# Initialization of sum and number of digits.
						snake_case_     =			0
						snake_case_     =			0
						snake_case_     =			n
						# Calculation of digits of the number
						while temp > 0:
												number_of_digits += 1
												temp //= 10
						# Dividing number into separate digits and find Armstrong number
						snake_case_     =			n
						while temp > 0:
												snake_case_     =			temp % 10
												total += rem**number_of_digits
												temp //= 10
						return n == total


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						if not isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ) or n < 1:
												return False

						# Init a "histogram" of the digits
						snake_case_     =			[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
						snake_case_     =			0
						snake_case_     =			0
						snake_case_     =			n
						while temp > 0:
												snake_case_,					snake_case_     =			divmod(SCREAMING_SNAKE_CASE__						,	10 )
												digit_histogram[rem] += 1
												digit_total += 1

						for cnt, i in zip(SCREAMING_SNAKE_CASE__						,	range(len(SCREAMING_SNAKE_CASE__ ) ) ):
												total += cnt * i**digit_total

						return n == total


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						if not isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ) or n < 1:
												return False
						snake_case_     =			len(str(SCREAMING_SNAKE_CASE__ ) )  # the power that all digits will be raised to
						# check if sum of each digit multiplied expo times is equal to number
						return n == sum(int(SCREAMING_SNAKE_CASE__ ) ** expo for i in str(SCREAMING_SNAKE_CASE__ ) )


def 						__SCREAMING_SNAKE_CASE      ():
						snake_case_     =			int(input('''Enter an integer to see if it is an Armstrong number: ''' ).strip() )
						print(F'''{num} is {'' if armstrong_number(SCREAMING_SNAKE_CASE__ ) else 'not '}an Armstrong number.''' )
						print(F'''{num} is {'' if narcissistic_number(SCREAMING_SNAKE_CASE__ ) else 'not '}an Armstrong number.''' )
						print(F'''{num} is {'' if pluperfect_number(SCREAMING_SNAKE_CASE__ ) else 'not '}an Armstrong number.''' )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()


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]							)