
"""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 SCREAMING_SNAKE_CASE       (      unittest.TestCase					):






	'''simple docstring'''







	snake_case__							:		Tuple         =		(JukeboxModel,) if is_torch_available() else ()
	snake_case__							:		Optional[Any]         =		'openai/jukebox-1b-lyrics'
	snake_case__							:		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__							:		str         =		[
	    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
	]

	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
	]

	snake_case__							:		str         =		[
	    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
	]
	snake_case__							:		List[str]         =		[
	    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
	]

	snake_case__							:		str         =		[
	    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
	]
	snake_case__							:		List[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
	]

	snake_case__							:		Tuple         =		[105_8304, 0, 78_6432, 7169, 507, 76, 27, 40, 30, 76]

	snake_case__							:		Optional[int]         =		[
	    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
	]
	snake_case__							:		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
	]
	snake_case__							:		Union[str, Any]         =		[
	    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
	]
	snake_case__							:		Tuple         =		[
	    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
	]
	snake_case__							:		str         =		[
	    -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__							:		Tuple         =		[
	    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__							:		Optional[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__							:		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 :Union[str, Any]			)			->   Optional[Any]:

			'''simple docstring'''






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


	@slow
	def    _UpperCamelCase							(  self :Dict			)			->   Optional[Any]:

			'''simple docstring'''






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

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

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

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


	@slow
	def    _UpperCamelCase							(  self :List[str]			)			->   Optional[int]:

			'''simple docstring'''






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

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

			a__   		=     model.priors[0]
			a__   		=     0
			a__   		=     top_prior.get_music_tokens_conds(__magic_name__       ,    start=__magic_name__       ,    end=start + top_prior.n_ctx			)
			a__   		=     top_prior.get_metadata(labels[0].clone()       ,    __magic_name__       ,    1058304       ,    0			)

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

			a__		,	a__		,	a__   		=     top_prior.get_cond(__magic_name__       ,    __magic_name__			)
			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 :int			)			->   List[Any]:

			'''simple docstring'''






			a__   		=     False

			a__   		=     JukeboxModel.from_pretrained(self.model_id       ,    min_duration=0			).eval()
			set_seed(0			)
			a__   		=     torch.rand((1, 5120, 1)			)
			a__   		=     list(self.prepare_inputs()			)

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

			a__   		=     torch.cat((zs[0], torch.zeros(1       ,    2048 - zs[0].shape[-1]			))       ,    dim=-1			).long()
			a__   		=     [upper_a, model.vqvae.encode(__magic_name__       ,    start_level=1       ,    bs_chunks=waveform.shape[0]			)[0], None]
			a__   		=     model._sample(
			    __magic_name__       ,    __magic_name__       ,    sample_levels=[1]       ,    save_results=__magic_name__       ,    sample_length=40 * model.priors[1].raw_to_tokens			)
			torch.testing.assert_allclose(zs[1][0][:40]       ,    torch.tensor(self.EXPECTED_PRIMED_1			)			)

			a__   		=     torch.cat((zs[1], torch.zeros(1       ,    2048 - zs[1].shape[-1]			))       ,    dim=-1			).long()
			a__   		=     [upper_a, upper_a, model.vqvae.encode(__magic_name__       ,    start_level=0       ,    bs_chunks=waveform.shape[0]			)[0]]
			a__   		=     model._sample(
			    __magic_name__       ,    __magic_name__       ,    sample_levels=[2]       ,    save_results=__magic_name__       ,    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 :List[Any]			)			->   List[Any]:

			'''simple docstring'''






			a__   		=     JukeboxModel.from_pretrained(self.model_id       ,    min_duration=0			).eval()
			set_seed(0			)
			a__   		=     torch.rand((1, 5120, 1)			)
			with torch.no_grad():
					a__   		=     model.vqvae.encode(__magic_name__       ,    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():
					a__   		=     model.vqvae.decode(__magic_name__       ,    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 SCREAMING_SNAKE_CASE       (      unittest.TestCase					):






	'''simple docstring'''







	snake_case__							:		str         =		(JukeboxModel,) if is_torch_available() else ()
	snake_case__							:		Optional[Any]         =		'openai/jukebox-5b-lyrics'
	snake_case__							:		Tuple         =		{
	    '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__							:		Tuple         =		[
	    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
	]
	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
	]

	snake_case__							:		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
	]
	snake_case__							:		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, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
	    416, 416, 416, 416, 416, 416, 416, 416
	]

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

	snake_case__							:		Tuple         =		[
	    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
	]
	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, 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
	]

	snake_case__							:		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
	]
	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    _UpperCamelCase							(  self :Tuple       ,    __magic_name__ :Tuple			)			->   int:

			'''simple docstring'''






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


	@slow
	def    _UpperCamelCase							(  self :int			)			->   List[Any]:

			'''simple docstring'''






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

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

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

			set_seed(0			)
			a__   		=     model._sample(__magic_name__       ,    __magic_name__       ,    [2]       ,    sample_length=60 * model.priors[2].raw_to_tokens       ,    save_results=__magic_name__			)
			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 :str			)			->   Union[str, Any]:

			'''simple docstring'''






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

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






	@slow
	def    _UpperCamelCase							(  self :Dict			)			->   Any:

			'''simple docstring'''






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

			a__   		=     self.prepare_inputs(__magic_name__			)[0].cuda()
			a__   		=     model.get_metadata(__magic_name__       ,    0       ,    7680       ,    0			)
			set_seed(0			)
			a__   		=     model.sample(1       ,    metadata=__magic_name__       ,    sample_tokens=60			)
			self.assertIn(outputs[0].cpu().tolist()       ,    [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]			)






"""simple docstring"""




import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation

require_version('''tokenizers>=0.13.3''')

if is_sentencepiece_available():
					from .tokenization_llama import LlamaTokenizer
else:
					__lowerCAmelCase							:  List[str] 				=		None

__lowerCAmelCase							:  Optional[int] 				=		logging.get_logger(__name__)
__lowerCAmelCase							:  int 				=		{'''vocab_file''': '''tokenizer.model''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCAmelCase				,__lowerCAmelCase							:  Any 				=		'''[INST]''', '''[/INST]'''
__lowerCAmelCase				,__lowerCAmelCase							:  Tuple 				=		'''<<SYS>>\n''', '''\n<</SYS>>\n\n'''

# fmt: off
__lowerCAmelCase							:  List[str] 				=		'''You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don\'t know the answer to a question, please don\'t share false information.'''
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	snake_case__							:		List[str]         =		VOCAB_FILES_NAMES
	snake_case__							:		Optional[Any]         =		LlamaTokenizer
	snake_case__							:		Union[str, Any]         =		'left'
	snake_case__							:		Any         =		['input_ids', 'attention_mask']


	def __init__(  self :Optional[Any]       ,    __magic_name__ :Optional[Any]=None       ,    __magic_name__ :Tuple=None       ,    __magic_name__ :List[Any]=False       ,    __magic_name__ :List[str]="<unk>"       ,    __magic_name__ :List[str]="<s>"       ,    __magic_name__ :List[str]="</s>"       ,    __magic_name__ :Any=True       ,    __magic_name__ :Union[str, Any]=False       ,    **__magic_name__ :Optional[int]       ,    )			->   List[Any]:

			'''simple docstring'''






			super().__init__(
			    vocab_file=__magic_name__       ,    tokenizer_file=__magic_name__       ,    clean_up_tokenization_spaces=__magic_name__       ,    unk_token=__magic_name__       ,    bos_token=__magic_name__       ,    eos_token=__magic_name__       ,    **__magic_name__       ,    )
			a__   		=     add_bos_token
			a__   		=     add_eos_token
			self.update_post_processor()

			a__   		=     vocab_file
			a__   		=     False if not self.vocab_file else True


	def    _UpperCamelCase							(  self :List[str]			)			->   Optional[int]:

			'''simple docstring'''






			a__   		=     self.bos_token
			a__   		=     self.bos_token_id

			a__   		=     self.eos_token
			a__   		=     self.eos_token_id

			a__   		=     F"{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"
			a__   		=     F"{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"

			a__   		=     []
			if self.add_bos_token:
					special_tokens.append((bos, bos_token_id)			)
			if self.add_eos_token:
					special_tokens.append((eos, eos_token_id)			)
			a__   		=     processors.TemplateProcessing(
			    single=__magic_name__       ,    pair=__magic_name__       ,    special_tokens=__magic_name__			)


	@property
	def    _UpperCamelCase							(  self :Optional[int]			)			->   Any:

			'''simple docstring'''






			return self._add_eos_token


	@property
	def    _UpperCamelCase							(  self :Optional[int]			)			->   Optional[Any]:

			'''simple docstring'''






			return self._add_bos_token


	@add_eos_token.setter
	def    _UpperCamelCase							(  self :int       ,    __magic_name__ :int			)			->   int:

			'''simple docstring'''






			a__   		=     value
			self.update_post_processor()


	@add_bos_token.setter
	def    _UpperCamelCase							(  self :Any       ,    __magic_name__ :str			)			->   Optional[int]:

			'''simple docstring'''






			a__   		=     value
			self.update_post_processor()


	def    _UpperCamelCase							(  self :Dict       ,    __magic_name__ :str       ,    __magic_name__ :Optional[str] = None			)			->   Tuple[str]:

			'''simple docstring'''






			if not self.can_save_slow_tokenizer:
					raise ValueError(
					    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
					    '''tokenizer.'''			)

			if not os.path.isdir(__magic_name__			):
					logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
					return
			a__   		=     os.path.join(
			    __magic_name__       ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

			if os.path.abspath(self.vocab_file			) != os.path.abspath(__magic_name__			):
					copyfile(self.vocab_file       ,    __magic_name__			)

			return (out_vocab_file,)






	def    _UpperCamelCase							(  self :int       ,    __magic_name__ :"Conversation"			)			->   List[Any]:

			'''simple docstring'''






			a__   		=     list(conversation.iter_texts()			)
			if not all([is_user for is_user, msg in dialogue[::2]]			) or not all(
			    [not is_user for is_user, msg in dialogue[1::2]]			):
					raise ValueError(
					    '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''			)

			a__   		=     []
			if len(conversation.past_user_inputs			) > 0:
					if not conversation.past_user_inputs[0].startswith(__magic_name__			) or E_SYS not in conversation.past_user_inputs[0]:
							a__   		=     (
							    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
							)
			elif not dialogue[0][1].startswith(__magic_name__			) or E_SYS not in dialogue[0][1]:
					a__   		=     (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

			dialog_tokens += sum(
			    [
			        [self.bos_token_id]
			        + self.encode(
			            F"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} "       ,    add_special_tokens=__magic_name__			)
			        + [self.eos_token_id]
			        for prompt, answer in zip(dialogue[::2]       ,    dialogue[1::2]			)
			    ]       ,    []       ,    )
			if not (dialogue[-1][0]):
					raise ValueError(F"Last message must be from user, got {dialogue[-1]['role']}"			)
			dialog_tokens += [self.bos_token_id] + self.encode(
			    F"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"       ,    add_special_tokens=__magic_name__			)
			return dialog_tokens




