





'''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 		UpperCamelCase__     (       unittest.TestCase							):
      '''simple docstring'''






      _snake_case           =				(JukeboxModel,) if is_torch_available() else ()
      _snake_case           =				'''openai/jukebox-1b-lyrics'''
      _snake_case           =				{
          '''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
      _snake_case           =				[
          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           =				[
          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           =				[
          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           =				[
          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           =				[
          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           =				[
          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           =				[105_8304, 0, 78_6432, 7169, 507, 76, 27, 40, 30, 76]

      _snake_case           =				[
          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           =				[
          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           =				[
          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           =				[
          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           =				[
          -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           =				[
          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           =				[
          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           =				[
          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 )				->  str:
        __lowerCAmelCase		:    Dict			     =  JukeboxTokenizer.from_pretrained(self.model_id )
        __lowerCAmelCase		:    Union[str, Any]			     =  tokenizer(**self.metas )['input_ids']
        return tokens




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

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

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

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




      @slow
      def 	snake_case  (     self )				->  Tuple:
        __lowerCAmelCase		:    Optional[int]			     =  False
        __lowerCAmelCase		:    Tuple			     =  JukeboxModel.from_pretrained(self.model_id     , min_duration=0 ).eval()

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

        __lowerCAmelCase		:    Optional[Any]			     =  model.priors[0]
        __lowerCAmelCase		:    List[str]			     =  0
        __lowerCAmelCase		:    Optional[Any]			     =  top_prior.get_music_tokens_conds(SCREAMING_SNAKE_CASE     , start=SCREAMING_SNAKE_CASE     , end=start + top_prior.n_ctx )
        __lowerCAmelCase		:    Optional[Any]			     =  top_prior.get_metadata(labels[0].clone()     , SCREAMING_SNAKE_CASE     , 1_05_83_04     , 0 )

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

        __lowerCAmelCase     ,					__lowerCAmelCase     ,					__lowerCAmelCase		:    Any			     =  top_prior.get_cond(SCREAMING_SNAKE_CASE     , SCREAMING_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 	snake_case  (     self )				->  List[Any]:
        __lowerCAmelCase		:    Tuple			     =  False

        __lowerCAmelCase		:    Optional[int]			     =  JukeboxModel.from_pretrained(self.model_id     , min_duration=0 ).eval()
        set_seed(0 )
        __lowerCAmelCase		:    int			     =  torch.rand((1, 51_20, 1) )
        __lowerCAmelCase		:    Tuple			     =  list(self.prepare_inputs() )

        __lowerCAmelCase		:    Tuple			     =  [model.vqvae.encode(SCREAMING_SNAKE_CASE     , start_level=2     , bs_chunks=waveform.shape[0] )[0], None, None]
        __lowerCAmelCase		:    Optional[Any]			     =  model._sample(
            SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , sample_levels=[0]     , save_results=SCREAMING_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 ) )

        __lowerCAmelCase		:    Union[str, Any]			     =  torch.cat((zs[0], torch.zeros(1     , 20_48 - zs[0].shape[-1] ))     , dim=-1 ).long()
        __lowerCAmelCase		:    int			     =  [upper_a, model.vqvae.encode(SCREAMING_SNAKE_CASE     , start_level=1     , bs_chunks=waveform.shape[0] )[0], None]
        __lowerCAmelCase		:    int			     =  model._sample(
            SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , sample_levels=[1]     , save_results=SCREAMING_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 ) )

        __lowerCAmelCase		:    Optional[Any]			     =  torch.cat((zs[1], torch.zeros(1     , 20_48 - zs[1].shape[-1] ))     , dim=-1 ).long()
        __lowerCAmelCase		:    List[Any]			     =  [upper_a, upper_a, model.vqvae.encode(SCREAMING_SNAKE_CASE     , start_level=0     , bs_chunks=waveform.shape[0] )[0]]
        __lowerCAmelCase		:    List[str]			     =  model._sample(
            SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , sample_levels=[2]     , save_results=SCREAMING_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 	snake_case  (     self )				->  Tuple:
        __lowerCAmelCase		:    str			     =  JukeboxModel.from_pretrained(self.model_id     , min_duration=0 ).eval()
        set_seed(0 )
        __lowerCAmelCase		:    Dict			     =  torch.rand((1, 51_20, 1) )
        with torch.no_grad():
          __lowerCAmelCase		:    str			     =  model.vqvae.encode(SCREAMING_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():
          __lowerCAmelCase		:    Tuple			     =  model.vqvae.decode(SCREAMING_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 		UpperCamelCase__     (       unittest.TestCase							):
      '''simple docstring'''






      _snake_case           =				(JukeboxModel,) if is_torch_available() else ()
      _snake_case           =				'''openai/jukebox-5b-lyrics'''
      _snake_case           =				{
          '''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
      _snake_case           =				[
          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           =				[
          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           =				[
          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           =				[
          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           =				[
          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           =				[
          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           =				[
          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           =				[
          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           =				[
          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           =				[
          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     , SCREAMING_SNAKE_CASE )				->  List[str]:
        __lowerCAmelCase		:    List[Any]			     =  JukeboxTokenizer.from_pretrained(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Optional[int]			     =  tokenizer(**self.metas )['input_ids']
        return tokens




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

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

        set_seed(0 )
        __lowerCAmelCase		:    Optional[Any]			     =  model._sample(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , [1]     , sample_length=60 * model.priors[1].raw_to_tokens     , save_results=SCREAMING_SNAKE_CASE )
        self.assertIn(zs[1][0].detach().cpu().tolist()     , [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2] )

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

        set_seed(0 )
        model.priors[0].cuda()
        __lowerCAmelCase		:    Optional[int]			     =  [torch.zeros(1     , 0     , dtype=torch.long ).cuda() for _ in range(3 )]
        __lowerCAmelCase		:    List[Any]			     =  model._sample(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , [0]     , sample_length=60 * model.priors[0].raw_to_tokens     , save_results=SCREAMING_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()
        __lowerCAmelCase		:    List[str]			     =  model._sample(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , [1]     , sample_length=60 * model.priors[1].raw_to_tokens     , save_results=SCREAMING_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()
        __lowerCAmelCase		:    Optional[int]			     =  model._sample(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , [2]     , sample_length=60 * model.priors[2].raw_to_tokens     , save_results=SCREAMING_SNAKE_CASE )
        torch.testing.assert_allclose(zs[2][0].cpu()     , torch.tensor(self.EXPECTED_GPU_OUTPUTS_0 ) )







      @slow
      def 	snake_case  (     self )				->  Optional[int]:
        __lowerCAmelCase		:    int			     =  'ArthurZ/jukebox_prior_0'
        __lowerCAmelCase		:    Dict			     =  JukeboxPrior.from_pretrained(SCREAMING_SNAKE_CASE     , min_duration=0 ).eval().half().to('cuda' )

        __lowerCAmelCase		:    str			     =  self.prepare_inputs(SCREAMING_SNAKE_CASE )[0].cuda()
        __lowerCAmelCase		:    List[str]			     =  model.get_metadata(SCREAMING_SNAKE_CASE     , 0     , 76_80     , 0 )
        set_seed(0 )
        __lowerCAmelCase		:    Tuple			     =  model.sample(1     , metadata=SCREAMING_SNAKE_CASE     , 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_tf_available,
    is_torch_available,
    is_vision_available,
)


A_				     =							{
    "configuration_sam": [
        "SAM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SamConfig",
        "SamMaskDecoderConfig",
        "SamPromptEncoderConfig",
        "SamVisionConfig",
    ],
    "processing_sam": ["SamProcessor"],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A_				     =							[
        "SAM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SamModel",
        "SamPreTrainedModel",
    ]
try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A_				     =							[
        "TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFSamModel",
        "TFSamPreTrainedModel",
    ]
try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A_				     =							["SamImageProcessor"]


if TYPE_CHECKING:
    from .configuration_sam import (
        SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SamConfig,
        SamMaskDecoderConfig,
        SamPromptEncoderConfig,
        SamVisionConfig,
    )
    from .processing_sam import SamProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_sam import SamImageProcessor

else:
    import sys

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





