"""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 	lowerCAmelCase_     (      unittest.TestCase       ):



				"""simple docstring"""
				_lowerCAmelCase  :     Tuple      	=    (JukeboxModel,) if is_torch_available() else ()
				_lowerCAmelCase  :     Dict      	=    """openai/jukebox-1b-lyrics"""
				_lowerCAmelCase  :     List[str]      	=    {
				    """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
				_lowerCAmelCase  :     Tuple      	=    [
				    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
				]

				_lowerCAmelCase  :     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
				]

				_lowerCAmelCase  :     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
				]
				_lowerCAmelCase  :     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
				]

				_lowerCAmelCase  :     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
				]
				_lowerCAmelCase  :     str      	=    [
				    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
				]

				_lowerCAmelCase  :     Optional[Any]      	=    [1058304, 0, 786432, 7169, 507, 76, 27, 40, 30, 76]

				_lowerCAmelCase  :     Optional[Any]      	=    [
				    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
				]
				_lowerCAmelCase  :     Optional[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
				]
				_lowerCAmelCase  :     List[str]      	=    [
				    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
				]
				_lowerCAmelCase  :     Optional[Any]      	=    [
				    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
				]
				_lowerCAmelCase  :     Union[str, Any]      	=    [
				    -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
				]
				_lowerCAmelCase  :     Tuple      	=    [
				    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
				]
				_lowerCAmelCase  :     int      	=    [
				    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
				]
				_lowerCAmelCase  :     str      	=    [
				    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     ):






										"""simple docstring"""

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

				@slow
				def 		snake_case			(					self     ):






										"""simple docstring"""

										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(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             =      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             =      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 		snake_case			(					self     ):






										"""simple docstring"""

										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(lowerCAmelCase		,     start=lowerCAmelCase		,     end=start + top_prior.n_ctx     )
										snake_case             =      top_prior.get_metadata(labels[0].clone()		,     lowerCAmelCase		,     1_05_83_04		,     0     )

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

										snake_case       ,snake_case       ,snake_case             =      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 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      False

										snake_case             =      JukeboxModel.from_pretrained(self.model_id		,     min_duration=0     ).eval()
										set_seed(0     )
										snake_case             =      torch.rand((1, 51_20, 1)     )
										snake_case             =      list(self.prepare_inputs()     )

										snake_case             =      [model.vqvae.encode(lowerCAmelCase		,     start_level=2		,     bs_chunks=waveform.shape[0]     )[0], None, None]
										snake_case             =      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             =      torch.cat((zs[0], torch.zeros(1		,     20_48 - zs[0].shape[-1]     ))		,     dim=-1     ).long()
										snake_case             =      [upper_a, model.vqvae.encode(lowerCAmelCase		,     start_level=1		,     bs_chunks=waveform.shape[0]     )[0], None]
										snake_case             =      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             =      torch.cat((zs[1], torch.zeros(1		,     20_48 - zs[1].shape[-1]     ))		,     dim=-1     ).long()
										snake_case             =      [upper_a, upper_a, model.vqvae.encode(lowerCAmelCase		,     start_level=0		,     bs_chunks=waveform.shape[0]     )[0]]
										snake_case             =      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 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      JukeboxModel.from_pretrained(self.model_id		,     min_duration=0     ).eval()
										set_seed(0     )
										snake_case             =      torch.rand((1, 51_20, 1)     )
										with torch.no_grad():
																snake_case             =      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             =      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 	lowerCAmelCase_     (      unittest.TestCase       ):



				"""simple docstring"""
				_lowerCAmelCase  :     Any      	=    (JukeboxModel,) if is_torch_available() else ()
				_lowerCAmelCase  :     List[str]      	=    """openai/jukebox-5b-lyrics"""
				_lowerCAmelCase  :     Union[str, Any]      	=    {
				    """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
				_lowerCAmelCase  :     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, 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
				]
				_lowerCAmelCase  :     Union[str, 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
				]

				_lowerCAmelCase  :     Union[str, Any]      	=    [
				    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
				]
				_lowerCAmelCase  :     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
				]

				_lowerCAmelCase  :     List[str]      	=    [
				    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
				]
				_lowerCAmelCase  :     Optional[Any]      	=    [
				    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
				]

				_lowerCAmelCase  :     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, 653, 653, 653, 653, 653,
				    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
				    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
				]
				_lowerCAmelCase  :     List[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
				]

				_lowerCAmelCase  :     List[Any]      	=    [
				    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
				]
				_lowerCAmelCase  :     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		,     lowerCAmelCase     ):






										"""simple docstring"""

										snake_case             =      JukeboxTokenizer.from_pretrained(lowerCAmelCase     )
										snake_case             =      tokenizer(**self.metas     )['input_ids']
										return tokens

				@slow
				def 		snake_case			(					self     ):






										"""simple docstring"""

										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(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             =      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             =      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 		snake_case			(					self     ):






										"""simple docstring"""

										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(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             =      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             =      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 		snake_case			(					self     ):






										"""simple docstring"""

										snake_case             =      'ArthurZ/jukebox_prior_0'
										snake_case             =      JukeboxPrior.from_pretrained(lowerCAmelCase		,     min_duration=0     ).eval().half().to('cuda'     )

										snake_case             =      self.prepare_inputs(lowerCAmelCase     )[0].cuda()
										snake_case             =      model.get_metadata(lowerCAmelCase		,     0		,     76_80		,     0     )
										set_seed(0     )
										snake_case             =      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"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__       =	{
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["MarianTokenizer"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	[
					    "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "MarianForCausalLM",
					    "MarianModel",
					    "MarianMTModel",
					    "MarianPreTrainedModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
					from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_marian import MarianTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_marian import (
										    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MarianForCausalLM,
										    MarianModel,
										    MarianMTModel,
										    MarianPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
					import sys

					SCREAMING_SNAKE_CASE__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


