


'''simple docstring'''
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							):



   '''simple docstring'''






   _lowercase    						= (JukeboxModel,) if is_torch_available() else ()
   _lowercase    						= 'openai/jukebox-1b-lyrics'
   _lowercase    						= {
       '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
   _lowercase    						= [
       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
   ]

   _lowercase    						= [
       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
   ]

   _lowercase    						= [
       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
   ]
   _lowercase    						= [
       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
   ]

   _lowercase    						= [
       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
   ]
   _lowercase    						= [
       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
   ]

   _lowercase    						= [105_8304, 0, 78_6432, 7169, 507, 76, 27, 40, 30, 76]

   _lowercase    						= [
       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
   ]
   _lowercase    						= [
       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
   ]
   _lowercase    						= [
       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
   ]
   _lowercase    						= [
       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
   ]
   _lowercase    						= [
       -0.0_4_9_2, -0.0_5_2_4, -0.0_5_6_5, -0.0_6_4_0, -0.0_6_8_6, -0.0_6_8_4, -0.0_6_7_7, -0.0_6_6_4,
       -0.0_6_0_5, -0.0_4_9_0, -0.0_3_3_0, -0.0_1_6_8, -0.0_0_8_3, -0.0_0_7_5, -0.0_0_5_1, 0.0_0_2_5,
       0.0_1_3_6, 0.0_2_6_1, 0.0_3_8_6, 0.0_4_9_7, 0.0_5_8_0, 0.0_5_9_9, 0.0_5_8_3, 0.0_6_1_4,
       0.0_7_4_0, 0.0_8_8_9, 0.1_0_2_3, 0.1_1_6_2, 0.1_2_1_1, 0.1_2_1_2, 0.1_2_5_1, 0.1_3_3_6,
       0.1_5_0_2, 0.1_6_8_6, 0.1_8_8_3, 0.2_1_4_8, 0.2_3_6_3, 0.2_4_5_8, 0.2_5_0_7, 0.2_5_3_1
   ]
   _lowercase    						= [
       0.0_2_5_6, -0.0_5_4_4, 0.1_6_0_0, -0.0_0_3_2, 0.1_0_6_6, 0.0_8_2_5, -0.0_0_1_3, 0.3_4_4_0,
       0.0_2_1_0, 0.0_4_1_2, -0.1_7_7_7, -0.0_8_9_2, -0.0_1_6_4, 0.0_2_8_5, -0.0_6_1_3, -0.0_6_1_7,
       -0.0_1_3_7, -0.0_2_0_1, -0.0_1_7_5, 0.0_2_1_5, -0.0_6_2_7, 0.0_5_2_0, -0.0_7_3_0, 0.0_9_7_0,
       -0.0_1_0_0, 0.0_4_4_2, -0.0_5_8_6, 0.0_2_0_7, -0.0_0_1_5, -0.0_0_8_2
   ]
   _lowercase    						= [
       0.0_4_1_5, 0.0_8_7_7, 0.0_0_2_2, -0.0_0_5_5, 0.0_7_5_1, 0.0_3_3_4, 0.0_3_2_4, -0.0_0_6_8,
       0.0_0_1_1, 0.0_0_1_7, -0.0_6_7_6, 0.0_6_5_5, -0.0_1_4_3, 0.0_3_9_9, 0.0_3_0_3, 0.0_7_4_3,
       -0.0_1_6_8, -0.0_3_9_4, -0.1_1_1_3, 0.0_1_2_4, 0.0_4_4_2, 0.0_2_6_7, -0.0_0_0_3, -0.1_5_3_6,
       -0.0_1_1_6, -0.1_8_3_7, -0.0_1_8_0, -0.1_0_2_6, -0.0_7_7_7, -0.0_4_5_6
   ]
   _lowercase    						= [
       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   __lowerCAmelCase						(	self	:					Union[str, Any]						)->	Optional[int]:
          snake_case__					:	int          =					JukeboxTokenizer.from_pretrained(self.model_id						)
          snake_case__					:	Tuple          =					tokenizer(**self.metas						)["""input_ids"""]
          return tokens




   @slow
   def   __lowerCAmelCase						(	self	:					Dict						)->	Dict:
          snake_case__					:	List[str]          =					JukeboxModel.from_pretrained(self.model_id					, min_duration=0						).eval()
          snake_case__					:	int          =					self.prepare_inputs()

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

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

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




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

          snake_case__					:	List[str]          =					self.prepare_inputs()
          set_seed(0						)
          snake_case__					:	List[Any]          =					[torch.zeros(1					, 0					, dtype=torch.long						) for _ in range(3						)]

          snake_case__					:	Dict          =					model.priors[0]
          snake_case__					:	Optional[int]          =					0
          snake_case__					:	Tuple          =					top_prior.get_music_tokens_conds(lowerCamelCase					, start=lowerCamelCase					, end=start + top_prior.n_ctx						)
          snake_case__					:	Tuple          =					top_prior.get_metadata(labels[0].clone()					, lowerCamelCase					, 1_058_304					, 0						)

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

          snake_case__       ,   snake_case__       ,   snake_case__					:	int          =					top_prior.get_cond(lowerCamelCase					, lowerCamelCase						)
          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   __lowerCAmelCase						(	self	:					Union[str, Any]						)->	Tuple:
          snake_case__					:	Any          =					False

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

          snake_case__					:	List[str]          =					[model.vqvae.encode(lowerCamelCase					, start_level=2					, bs_chunks=waveform.shape[0]						)[0], None, None]
          snake_case__					:	Optional[Any]          =					model._sample(
              lowerCamelCase					, lowerCamelCase					, sample_levels=[0]					, save_results=lowerCamelCase					, sample_length=40 * model.priors[0].raw_to_tokens						)
          torch.testing.assert_allclose(zs[0][0][:40]					, torch.tensor(self.EXPECTED_PRIMED_0						)						)

          snake_case__					:	Union[str, Any]          =					torch.cat((zs[0], torch.zeros(1					, 2_048 - zs[0].shape[-1]						))					, dim=-1						).long()
          snake_case__					:	Tuple          =					[upper_a, model.vqvae.encode(lowerCamelCase					, start_level=1					, bs_chunks=waveform.shape[0]						)[0], None]
          snake_case__					:	List[Any]          =					model._sample(
              lowerCamelCase					, lowerCamelCase					, sample_levels=[1]					, save_results=lowerCamelCase					, sample_length=40 * model.priors[1].raw_to_tokens						)
          torch.testing.assert_allclose(zs[1][0][:40]					, torch.tensor(self.EXPECTED_PRIMED_1						)						)

          snake_case__					:	int          =					torch.cat((zs[1], torch.zeros(1					, 2_048 - zs[1].shape[-1]						))					, dim=-1						).long()
          snake_case__					:	Optional[int]          =					[upper_a, upper_a, model.vqvae.encode(lowerCamelCase					, start_level=0					, bs_chunks=waveform.shape[0]						)[0]]
          snake_case__					:	Any          =					model._sample(
              lowerCamelCase					, lowerCamelCase					, sample_levels=[2]					, save_results=lowerCamelCase					, 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   __lowerCAmelCase						(	self	:					List[Any]						)->	Optional[int]:
          snake_case__					:	Union[str, Any]          =					JukeboxModel.from_pretrained(self.model_id					, min_duration=0						).eval()
          set_seed(0						)
          snake_case__					:	Optional[Any]          =					torch.rand((1, 5_120, 1)						)
          with torch.no_grad():
                 snake_case__					:	Any          =					model.vqvae.encode(lowerCamelCase					, 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__					:	str          =					model.vqvae.decode(lowerCamelCase					, 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							):



   '''simple docstring'''






   _lowercase    						= (JukeboxModel,) if is_torch_available() else ()
   _lowercase    						= 'openai/jukebox-5b-lyrics'
   _lowercase    						= {
       '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
   _lowercase    						= [
       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
   ]
   _lowercase    						= [
       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
   ]

   _lowercase    						= [
       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
   ]
   _lowercase    						= [
       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
   ]

   _lowercase    						= [
       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
   ]
   _lowercase    						= [
       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
   ]

   _lowercase    						= [
       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
   ]
   _lowercase    						= [
       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
   ]

   _lowercase    						= [
       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
   ]
   _lowercase    						= [
       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   __lowerCAmelCase						(	self	:					List[str]					, lowerCamelCase	:					Optional[Any]						)->	Tuple:
          snake_case__					:	Optional[int]          =					JukeboxTokenizer.from_pretrained(lowerCamelCase						)
          snake_case__					:	List[Any]          =					tokenizer(**self.metas						)["""input_ids"""]
          return tokens




   @slow
   def   __lowerCAmelCase						(	self	:					str						)->	Optional[int]:
          snake_case__					:	List[str]          =					JukeboxModel.from_pretrained(self.model_id					, min_duration=0						).eval()
          snake_case__					:	Optional[int]          =					self.prepare_inputs(self.model_id						)

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

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

          set_seed(0						)
          snake_case__					:	Optional[int]          =					model._sample(lowerCamelCase					, lowerCamelCase					, [2]					, sample_length=60 * model.priors[2].raw_to_tokens					, save_results=lowerCamelCase						)
          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   __lowerCAmelCase						(	self	:					Optional[Any]						)->	int:
          snake_case__					:	Tuple          =					JukeboxModel.from_pretrained(self.model_id					, min_duration=0						).eval()
          snake_case__					:	Tuple          =					[i.cuda() for i in self.prepare_inputs(self.model_id						)]

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




   @slow
   def   __lowerCAmelCase						(	self	:					str						)->	Union[str, Any]:
          snake_case__					:	List[str]          =					"""ArthurZ/jukebox_prior_0"""
          snake_case__					:	Optional[Any]          =					JukeboxPrior.from_pretrained(lowerCamelCase					, min_duration=0						).eval().half().to("""cuda"""						)

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








'''simple docstring'''
import unittest
from timeit import timeit



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Any          =					first_num if first_num >= second_num else second_num
       snake_case__					:	Optional[Any]          =					max_num
       while (common_mult % first_num > 0) or (common_mult % second_num > 0):
              common_mult += max_num
       return common_mult



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       return b if a == 0 else greatest_common_divisor(b % a    ,       UpperCAmelCase     )



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       return first_num // greatest_common_divisor(UpperCAmelCase    ,       UpperCAmelCase     ) * second_num



def 				lowerCAmelCase__	(       ):






       """simple docstring"""





       snake_case__					:	Tuple          =					(
           """from __main__ import least_common_multiple_slow, least_common_multiple_fast"""
       )
       print(
           """least_common_multiple_slow():"""    ,       timeit("""least_common_multiple_slow(1000, 999)"""    ,       setup=UpperCAmelCase     )    ,       )
       print(
           """least_common_multiple_fast():"""    ,       timeit("""least_common_multiple_fast(1000, 999)"""    ,       setup=UpperCAmelCase     )    ,       )







class 					_A						(						unittest.TestCase							):



   '''simple docstring'''






   _lowercase    						= (
       (10, 20),
       (13, 15),
       (4, 31),
       (10, 42),
       (43, 34),
       (5, 12),
       (12, 25),
       (10, 25),
       (6, 9),
   )
   _lowercase    						= (20, 195, 124, 210, 1462, 60, 300, 50, 18)




   def   __lowerCAmelCase						(	self	:					Optional[int]						)->	Optional[Any]:
          for i, (first_num, second_num) in enumerate(self.test_inputs						):
                 snake_case__					:	List[Any]          =					least_common_multiple_slow(lowerCamelCase					, lowerCamelCase						)
                 snake_case__					:	Optional[Any]          =					least_common_multiple_fast(lowerCamelCase					, lowerCamelCase						)
                 with self.subTest(i=lowerCamelCase						):
                        self.assertEqual(lowerCamelCase					, self.expected_results[i]						)
                        self.assertEqual(lowerCamelCase					, self.expected_results[i]						)


if __name__ == "__main__":
     benchmark()
     unittest.main()




