




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 					__lowerCAmelCase				( unittest.TestCase				):
			snake_case_   :				Optional[int]				  =  (JukeboxModel,) if is_torch_available() else ()
			snake_case_   :				int				  =  "openai/jukebox-1b-lyrics"
			snake_case_   :				Dict				  =  {
			    "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
			snake_case_   :				Union[str, Any]				  =  [
			    1_864, 1_536, 1_213, 1_870, 1_357, 1_536, 519, 880, 1_323, 789, 1_082, 534,
			    1_000, 1_445, 1_105, 1_130, 967, 515, 1_434, 1_620, 534, 1_495, 283, 1_445,
			    333, 1_307, 539, 1_631, 1_528, 375, 1_434, 673, 627, 710, 778, 1_883,
			    1_405, 1_276, 1_455, 1_228
			]

			snake_case_   :				Union[str, Any]				  =  [
			    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653
			]

			snake_case_   :				int				  =  [
			    1_125, 1_751, 697, 1_776, 1_141, 1_476, 391, 697, 1_125, 684, 867, 416,
			    844, 1_372, 1_274, 717, 1_274, 844, 1_299, 1_419, 697, 1_370, 317, 1_125,
			    191, 1_440, 1_370, 1_440, 1_370, 282, 1_621, 1_370, 368, 349, 867, 1_872,
			    1_262, 869, 1_728, 747
			]
			snake_case_   :				str				  =  [
			    416, 416, 1_125, 1_125, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416
			]

			snake_case_   :				int				  =  [
			    1_755, 842, 307, 1_843, 1_022, 1_395, 234, 1_554, 806, 739, 1_022, 442,
			    616, 556, 268, 1_499, 933, 457, 1_440, 1_837, 755, 985, 308, 902,
			    293, 1_443, 1_671, 1_141, 1_533, 555, 1_562, 1_061, 287, 417, 1_022, 2_008,
			    1_186, 1_015, 1_777, 268
			]
			snake_case_   :				Union[str, Any]				  =  [
			    854, 842, 1_353, 114, 1_353, 842, 185, 842, 185, 114, 591, 842,
			    185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842,
			    591, 842, 1_353, 842, 185, 842, 591, 842, 591, 114, 591, 842,
			    185, 842, 591, 89
			]

			snake_case_   :				Optional[int]				  =  [1_058_304, 0, 786_432, 7_169, 507, 76, 27, 40, 30, 76]

			snake_case_   :				List[str]				  =  [
			    390, 1_160, 1_002, 1_907, 1_788, 1_788, 1_788, 1_907, 1_002, 1_002, 1_854, 1_002,
			    1_002, 1_002, 1_002, 1_002, 1_002, 1_160, 1_160, 1_606, 596, 596, 1_160, 1_002,
			    1_516, 596, 1_002, 1_002, 1_002, 1_907, 1_788, 1_788, 1_788, 1_854, 1_788, 1_907,
			    1_907, 1_788, 596, 1_626
			]
			snake_case_   :				Optional[int]				  =  [
			    1_236, 1_668, 1_484, 1_920, 1_848, 1_409, 139, 864, 1_828, 1_272, 1_599, 824,
			    1_672, 139, 555, 1_484, 824, 1_920, 555, 596, 1_579, 1_599, 1_231, 1_599,
			    1_637, 1_407, 212, 824, 1_599, 116, 1_433, 824, 258, 1_599, 1_433, 1_895,
			    1_063, 1_433, 1_433, 1_599
			]
			snake_case_   :				Union[str, Any]				  =  [
			    1_684, 1_873, 1_119, 1_189, 395, 611, 1_901, 972, 890, 1_337, 1_392, 1_927,
			    96, 972, 672, 780, 1_119, 890, 158, 771, 1_073, 1_927, 353, 1_331,
			    1_269, 1_459, 1_333, 1_645, 812, 1_577, 1_337, 606, 353, 981, 1_466, 619,
			    197, 391, 302, 1_930
			]
			snake_case_   :				List[Any]				  =  [
			    390, 1_160, 1_002, 1_907, 1_788, 1_788, 1_788, 1_907, 1_002, 1_002, 1_854, 1_002,
			    1_002, 1_002, 1_002, 1_002, 1_002, 1_160, 1_160, 1_606, 596, 596, 1_160, 1_002,
			    1_516, 596, 1_002, 1_002, 1_002, 1_907, 1_788, 1_788, 1_788, 1_854, 1_788, 1_907,
			    1_907, 1_788, 596, 1_626
			]
			snake_case_   :				Tuple				  =  [
			    -0.0_492, -0.0_524, -0.0_565, -0.0_640, -0.0_686, -0.0_684, -0.0_677, -0.0_664,
			    -0.0_605, -0.0_490, -0.0_330, -0.0_168, -0.0_083, -0.0_075, -0.0_051, 0.0_025,
			    0.0_136, 0.0_261, 0.0_386, 0.0_497, 0.0_580, 0.0_599, 0.0_583, 0.0_614,
			    0.0_740, 0.0_889, 0.1_023, 0.1_162, 0.1_211, 0.1_212, 0.1_251, 0.1_336,
			    0.1_502, 0.1_686, 0.1_883, 0.2_148, 0.2_363, 0.2_458, 0.2_507, 0.2_531
			]
			snake_case_   :				Optional[int]				  =  [
			    0.0_256, -0.0_544, 0.1_600, -0.0_032, 0.1_066, 0.0_825, -0.0_013, 0.3_440,
			    0.0_210, 0.0_412, -0.1_777, -0.0_892, -0.0_164, 0.0_285, -0.0_613, -0.0_617,
			    -0.0_137, -0.0_201, -0.0_175, 0.0_215, -0.0_627, 0.0_520, -0.0_730, 0.0_970,
			    -0.0_100, 0.0_442, -0.0_586, 0.0_207, -0.0_015, -0.0_082
			]
			snake_case_   :				List[Any]				  =  [
			    0.0_415, 0.0_877, 0.0_022, -0.0_055, 0.0_751, 0.0_334, 0.0_324, -0.0_068,
			    0.0_011, 0.0_017, -0.0_676, 0.0_655, -0.0_143, 0.0_399, 0.0_303, 0.0_743,
			    -0.0_168, -0.0_394, -0.1_113, 0.0_124, 0.0_442, 0.0_267, -0.0_003, -0.1_536,
			    -0.0_116, -0.1_837, -0.0_180, -0.1_026, -0.0_777, -0.0_456
			]
			snake_case_   :				Optional[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 UpperCamelCase    ( self					:    str   ):




									"""simple docstring"""


									_UpperCAmelCase									=  JukeboxTokenizer.from_pretrained(self.model_id   )
									_UpperCAmelCase									=  tokenizer(**self.metas   )["input_ids"]
									return tokens







			@slow
			def UpperCamelCase    ( self					:    Any   ):




									"""simple docstring"""


									_UpperCAmelCase									=  JukeboxModel.from_pretrained(self.model_id					,					min_duration=0   ).eval()
									_UpperCAmelCase									=  self.prepare_inputs()

									set_seed(0   )
									_UpperCAmelCase									=  [torch.zeros(1					,					0					,					dtype=torch.long   ).cpu() for _ in range(3   )]
									_UpperCAmelCase									=  model._sample(snake_case__					,					snake_case__					,					[0]					,					sample_length=40 * model.priors[0].raw_to_tokens					,					save_results=snake_case__   )
									self.assertIn(zs[0][0].detach().cpu().tolist()					,					[self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]   )

									set_seed(0   )
									_UpperCAmelCase									=  model._sample(snake_case__					,					snake_case__					,					[1]					,					sample_length=40 * model.priors[1].raw_to_tokens					,					save_results=snake_case__   )
									self.assertIn(zs[1][0].detach().cpu().tolist()					,					[self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]   )

									set_seed(0   )
									_UpperCAmelCase									=  model._sample(snake_case__					,					snake_case__					,					[2]					,					sample_length=40 * model.priors[2].raw_to_tokens					,					save_results=snake_case__   )
									self.assertIn(zs[2][0].detach().cpu().tolist()					,					[self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]   )







			@slow
			def UpperCamelCase    ( self					:    List[Any]   ):




									"""simple docstring"""


									_UpperCAmelCase									=  False
									_UpperCAmelCase									=  JukeboxModel.from_pretrained(self.model_id					,					min_duration=0   ).eval()

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

									_UpperCAmelCase									=  model.priors[0]
									_UpperCAmelCase									=  0
									_UpperCAmelCase									=  top_prior.get_music_tokens_conds(snake_case__					,					start=snake_case__					,					end=start + top_prior.n_ctx   )
									_UpperCAmelCase									=  top_prior.get_metadata(labels[0].clone()					,					snake_case__					,					1_058_304					,					0   )

									self.assertIsNone(snake_case__   )
									self.assertListEqual(metadata.numpy()[0][:10].tolist()					,					self.EXPECTED_Y_COND   )

									_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase									=  top_prior.get_cond(snake_case__					,					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 UpperCamelCase    ( self					:    Optional[int]   ):




									"""simple docstring"""


									_UpperCAmelCase									=  False

									_UpperCAmelCase									=  JukeboxModel.from_pretrained(self.model_id					,					min_duration=0   ).eval()
									set_seed(0   )
									_UpperCAmelCase									=  torch.rand((1, 5_120, 1)   )
									_UpperCAmelCase									=  list(self.prepare_inputs()   )

									_UpperCAmelCase									=  [model.vqvae.encode(snake_case__					,					start_level=2					,					bs_chunks=waveform.shape[0]   )[0], None, None]
									_UpperCAmelCase									=  model._sample(
									    snake_case__					,					snake_case__					,					sample_levels=[0]					,					save_results=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   )   )

									_UpperCAmelCase									=  torch.cat((zs[0], torch.zeros(1					,					2_048 - zs[0].shape[-1]   ))					,					dim=-1   ).long()
									_UpperCAmelCase									=  [upper_a, model.vqvae.encode(snake_case__					,					start_level=1					,					bs_chunks=waveform.shape[0]   )[0], None]
									_UpperCAmelCase									=  model._sample(
									    snake_case__					,					snake_case__					,					sample_levels=[1]					,					save_results=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   )   )

									_UpperCAmelCase									=  torch.cat((zs[1], torch.zeros(1					,					2_048 - zs[1].shape[-1]   ))					,					dim=-1   ).long()
									_UpperCAmelCase									=  [upper_a, upper_a, model.vqvae.encode(snake_case__					,					start_level=0					,					bs_chunks=waveform.shape[0]   )[0]]
									_UpperCAmelCase									=  model._sample(
									    snake_case__					,					snake_case__					,					sample_levels=[2]					,					save_results=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 UpperCamelCase    ( self					:    Tuple   ):




									"""simple docstring"""


									_UpperCAmelCase									=  JukeboxModel.from_pretrained(self.model_id					,					min_duration=0   ).eval()
									set_seed(0   )
									_UpperCAmelCase									=  torch.rand((1, 5_120, 1)   )
									with torch.no_grad():
															_UpperCAmelCase									=  model.vqvae.encode(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():
															_UpperCAmelCase									=  model.vqvae.decode(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 					__lowerCAmelCase				( unittest.TestCase				):
			snake_case_   :				str				  =  (JukeboxModel,) if is_torch_available() else ()
			snake_case_   :				Any				  =  "openai/jukebox-5b-lyrics"
			snake_case_   :				Optional[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
			snake_case_   :				Dict				  =  [
			    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    1_489, 1_489, 1_489, 1_489, 1_150, 1_853, 1_509, 1_150, 1_357, 1_509, 6, 1_272
			]
			snake_case_   :				Tuple				  =  [
			    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
			]

			snake_case_   :				Dict				  =  [
			    1_125, 416, 1_125, 1_125, 1_125, 1_125, 1_125, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
			]
			snake_case_   :				int				  =  [
			    416, 416, 1_125, 1_125, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416
			]

			snake_case_   :				Dict				  =  [
			    1_755, 1_061, 234, 1_755, 1_061, 1_755, 185, 290, 307, 307, 616, 616,
			    616, 616, 616, 616, 307, 290, 417, 1_755, 234, 1_755, 185, 290,
			    290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1_755,
			    234, 234, 1_755, 234, 185, 185, 307, 616, 616, 616, 616, 290,
			    1_755, 1_755, 1_755, 234, 234, 1_755, 1_572, 290, 307, 616, 34, 616
			]
			snake_case_   :				Dict				  =  [
			    854, 842, 1_353, 114, 1_353, 842, 185, 842, 185, 114, 591, 842, 185,
			    417, 185, 842, 307, 842, 591, 842, 185, 842, 185, 842, 591, 842,
			    1_353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591,
			    89, 591, 842, 591, 842, 591, 417, 1_372, 842, 1_372, 842, 34, 842,
			    185, 89, 591, 842, 185, 842, 591, 632
			]

			snake_case_   :				Optional[int]				  =  [
			    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
			]
			snake_case_   :				List[Any]				  =  [
			    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 1_853, 1_177, 1_536, 1_228,
			    710, 475, 1_489, 1_229, 1_224, 231, 1_224, 252, 1_434, 653, 475,
			    1_106, 1_877, 1_599, 1_228, 1_600, 1_683, 1_182, 1_853, 475, 1_864,
			    252, 1_229, 1_434, 2_001
			]

			snake_case_   :				Optional[Any]				  =  [
			    1_125, 1_125, 416, 1_125, 1_125, 416, 1_125, 1_125, 416, 416, 1_125, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
			]
			snake_case_   :				Tuple				  =  [
			    491, 1_755, 34, 1_613, 1_755, 417, 992, 1_613, 222, 842, 1_353, 1_613,
			    844, 632, 185, 1_613, 844, 632, 185, 1_613, 185, 842, 677, 1_613,
			    185, 114, 1_353, 1_613, 307, 89, 844, 1_613, 307, 1_332, 234, 1_979,
			    307, 89, 1_353, 616, 34, 842, 185, 842, 34, 842, 185, 842,
			    307, 114, 185, 89, 34, 1_268, 185, 89, 34, 842, 185, 89
			]







			def UpperCamelCase    ( self					:    str					,					snake_case__					:    Union[str, Any]   ):




									"""simple docstring"""


									_UpperCAmelCase									=  JukeboxTokenizer.from_pretrained(snake_case__   )
									_UpperCAmelCase									=  tokenizer(**self.metas   )["input_ids"]
									return tokens







			@slow
			def UpperCamelCase    ( self					:    Union[str, Any]   ):




									"""simple docstring"""


									_UpperCAmelCase									=  JukeboxModel.from_pretrained(self.model_id					,					min_duration=0   ).eval()
									_UpperCAmelCase									=  self.prepare_inputs(self.model_id   )

									set_seed(0   )
									_UpperCAmelCase									=  [torch.zeros(1					,					0					,					dtype=torch.long   ).cpu() for _ in range(3   )]
									_UpperCAmelCase									=  model._sample(snake_case__					,					snake_case__					,					[0]					,					sample_length=60 * model.priors[0].raw_to_tokens					,					save_results=snake_case__   )
									self.assertIn(zs[0][0].detach().cpu().tolist()					,					[self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]   )

									set_seed(0   )
									_UpperCAmelCase									=  model._sample(snake_case__					,					snake_case__					,					[1]					,					sample_length=60 * model.priors[1].raw_to_tokens					,					save_results=snake_case__   )
									self.assertIn(zs[1][0].detach().cpu().tolist()					,					[self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]   )

									set_seed(0   )
									_UpperCAmelCase									=  model._sample(snake_case__					,					snake_case__					,					[2]					,					sample_length=60 * model.priors[2].raw_to_tokens					,					save_results=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 UpperCamelCase    ( self					:    Any   ):




									"""simple docstring"""


									_UpperCAmelCase									=  JukeboxModel.from_pretrained(self.model_id					,					min_duration=0   ).eval()
									_UpperCAmelCase									=  [i.cuda() for i in self.prepare_inputs(self.model_id   )]

									set_seed(0   )
									model.priors[0].cuda()
									_UpperCAmelCase									=  [torch.zeros(1					,					0					,					dtype=torch.long   ).cuda() for _ in range(3   )]
									_UpperCAmelCase									=  model._sample(snake_case__					,					snake_case__					,					[0]					,					sample_length=60 * model.priors[0].raw_to_tokens					,					save_results=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()
									_UpperCAmelCase									=  model._sample(snake_case__					,					snake_case__					,					[1]					,					sample_length=60 * model.priors[1].raw_to_tokens					,					save_results=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()
									_UpperCAmelCase									=  model._sample(snake_case__					,					snake_case__					,					[2]					,					sample_length=60 * model.priors[2].raw_to_tokens					,					save_results=snake_case__   )
									torch.testing.assert_allclose(zs[2][0].cpu()					,					torch.tensor(self.EXPECTED_GPU_OUTPUTS_0   )   )







			@slow
			def UpperCamelCase    ( self					:    Union[str, Any]   ):




									"""simple docstring"""


									_UpperCAmelCase									=  "ArthurZ/jukebox_prior_0"
									_UpperCAmelCase									=  JukeboxPrior.from_pretrained(snake_case__					,					min_duration=0   ).eval().half().to("cuda"   )

									_UpperCAmelCase									=  self.prepare_inputs(snake_case__   )[0].cuda()
									_UpperCAmelCase									=  model.get_metadata(snake_case__					,					0					,					7_680					,					0   )
									set_seed(0   )
									_UpperCAmelCase									=  model.sample(1					,					metadata=snake_case__					,					sample_tokens=60   )
									self.assertIn(outputs[0].cpu().tolist()					,					[self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]   )










def 							__SCREAMING_SNAKE_CASE						(    snake_case_ = 99_8001		):

						'''simple docstring'''


						for number in range(n - 1			,					9999			,					-1		):
												_UpperCAmelCase									=  str(snake_case_		)

												# checks whether 'str_number' is a palindrome.
												if str_number == str_number[::-1]:
																		_UpperCAmelCase									=  999

																		# if 'number' is a product of two 3-digit numbers
																		# then number is the answer otherwise fetch next number.
																		while divisor != 99:
																								if (number % divisor == 0) and (len(str(number // divisor		)		) == 3.0):
																														return number
																								divisor -= 1
						raise ValueError("That number is larger than our acceptable range."		)


if __name__ == "__main__":
				print(f"""{solution() = }""")




