
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):
      _UpperCAmelCase    :   Optional[int]   	=      (JukeboxModel,) if is_torch_available() else ()
      _UpperCAmelCase    :   Any   	=      """openai/jukebox-1b-lyrics"""
      _UpperCAmelCase    :   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
      _UpperCAmelCase    :   Union[str, Any]   	=      [
          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
      ]

      _UpperCAmelCase    :   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
      ]

      _UpperCAmelCase    :   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
      ]
      _UpperCAmelCase    :   Union[str, 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
      ]

      _UpperCAmelCase    :   int   	=      [
          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
      ]
      _UpperCAmelCase    :   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
      ]

      _UpperCAmelCase    :   Optional[Any]   	=      [105_8304, 0, 78_6432, 7169, 507, 76, 27, 40, 30, 76]

      _UpperCAmelCase    :   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
      ]
      _UpperCAmelCase    :   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
      ]
      _UpperCAmelCase    :   List[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
      ]
      _UpperCAmelCase    :   Dict   	=      [
          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
      ]
      _UpperCAmelCase    :   Dict   	=      [
          -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
      ]
      _UpperCAmelCase    :   Optional[int]   	=      [
          0.0_256, -0.0_544, 0.1_600, -0.0_032, 0.1_066, 0.0_825, -0.0_013, 0.3_440,
          0.0_210, 0.0_412, -0.1_777, -0.0_892, -0.0_164, 0.0_285, -0.0_613, -0.0_617,
          -0.0_137, -0.0_201, -0.0_175, 0.0_215, -0.0_627, 0.0_520, -0.0_730, 0.0_970,
          -0.0_100, 0.0_442, -0.0_586, 0.0_207, -0.0_015, -0.0_082
      ]
      _UpperCAmelCase    :   List[Any]   	=      [
          0.0_415, 0.0_877, 0.0_022, -0.0_055, 0.0_751, 0.0_334, 0.0_324, -0.0_068,
          0.0_011, 0.0_017, -0.0_676, 0.0_655, -0.0_143, 0.0_399, 0.0_303, 0.0_743,
          -0.0_168, -0.0_394, -0.1_113, 0.0_124, 0.0_442, 0.0_267, -0.0_003, -0.1_536,
          -0.0_116, -0.1_837, -0.0_180, -0.1_026, -0.0_777, -0.0_456
      ]
      _UpperCAmelCase    :   Tuple   	=      [
          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				):
       lowerCamelCase		:     int          =				JukeboxTokenizer.from_pretrained(self.model_id				)
       lowerCamelCase		:     str          =				tokenizer(**self.metas				)["""input_ids"""]
       return tokens
      @slow
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[Any]          =				JukeboxModel.from_pretrained(self.model_id   ,  min_duration=0				).eval()
       lowerCamelCase		:     Any          =				self.prepare_inputs()

       set_seed(0				)
       lowerCamelCase		:     List[Any]          =				[torch.zeros(1   ,  0   ,  dtype=torch.long				).cpu() for _ in range(3				)]
       lowerCamelCase		:     List[Any]          =				model._sample(__magic_name__   ,  __magic_name__   ,  [0]   ,  sample_length=4_0 * 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				)
       lowerCamelCase		:     Dict          =				model._sample(__magic_name__   ,  __magic_name__   ,  [1]   ,  sample_length=4_0 * 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				)
       lowerCamelCase		:     Tuple          =				model._sample(__magic_name__   ,  __magic_name__   ,  [2]   ,  sample_length=4_0 * 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				):
       lowerCamelCase		:     List[str]          =				False
       lowerCamelCase		:     str          =				JukeboxModel.from_pretrained(self.model_id   ,  min_duration=0				).eval()

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

       lowerCamelCase		:     List[str]          =				model.priors[0]
       lowerCamelCase		:     List[Any]          =				0
       lowerCamelCase		:     List[str]          =				top_prior.get_music_tokens_conds(__magic_name__   ,  start=__magic_name__   ,  end=start + top_prior.n_ctx				)
       lowerCamelCase		:     List[str]          =				top_prior.get_metadata(labels[0].clone()   ,  __magic_name__   ,  1_0_5_8_3_0_4   ,  0				)

       self.assertIsNone(__magic_name__				)
       self.assertListEqual(metadata.numpy()[0][:1_0].tolist()   ,  self.EXPECTED_Y_COND				)

       lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase		:     Optional[Any]          =				top_prior.get_cond(__magic_name__   ,  __magic_name__				)
       torch.testing.assert_allclose(
           audio_conditioning[0][0][:3_0].detach()   ,  torch.tensor(self.EXPECTED_AUDIO_COND				)   ,  atol=1e-4   ,  rtol=1e-4				)
       torch.testing.assert_allclose(
           metadata_conditioning[0][0][:3_0].detach()   ,  torch.tensor(self.EXPECTED_META_COND				)   ,  atol=1e-4   ,  rtol=1e-4				)
       torch.testing.assert_allclose(
           lyric_tokens[0, :3_0].detach()   ,  torch.tensor(self.EXPECTED_LYRIC_COND				)   ,  atol=1e-4   ,  rtol=1e-4				)
      @slow
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[str]          =				False

       lowerCamelCase		:     int          =				JukeboxModel.from_pretrained(self.model_id   ,  min_duration=0				).eval()
       set_seed(0				)
       lowerCamelCase		:     str          =				torch.rand((1, 5_1_2_0, 1)				)
       lowerCamelCase		:     Union[str, Any]          =				list(self.prepare_inputs()				)

       lowerCamelCase		:     List[Any]          =				[model.vqvae.encode(__magic_name__   ,  start_level=2   ,  bs_chunks=waveform.shape[0]				)[0], None, None]
       lowerCamelCase		:     Any          =				model._sample(
           __magic_name__   ,  __magic_name__   ,  sample_levels=[0]   ,  save_results=__magic_name__   ,  sample_length=4_0 * model.priors[0].raw_to_tokens				)
       torch.testing.assert_allclose(zs[0][0][:4_0]   ,  torch.tensor(self.EXPECTED_PRIMED_0				)				)

       lowerCamelCase		:     Any          =				torch.cat((zs[0], torch.zeros(1   ,  2_0_4_8 - zs[0].shape[-1]				))   ,  dim=-1				).long()
       lowerCamelCase		:     Optional[int]          =				[upper_a, model.vqvae.encode(__magic_name__   ,  start_level=1   ,  bs_chunks=waveform.shape[0]				)[0], None]
       lowerCamelCase		:     Union[str, Any]          =				model._sample(
           __magic_name__   ,  __magic_name__   ,  sample_levels=[1]   ,  save_results=__magic_name__   ,  sample_length=4_0 * model.priors[1].raw_to_tokens				)
       torch.testing.assert_allclose(zs[1][0][:4_0]   ,  torch.tensor(self.EXPECTED_PRIMED_1				)				)

       lowerCamelCase		:     List[str]          =				torch.cat((zs[1], torch.zeros(1   ,  2_0_4_8 - zs[1].shape[-1]				))   ,  dim=-1				).long()
       lowerCamelCase		:     List[str]          =				[upper_a, upper_a, model.vqvae.encode(__magic_name__   ,  start_level=0   ,  bs_chunks=waveform.shape[0]				)[0]]
       lowerCamelCase		:     Optional[int]          =				model._sample(
           __magic_name__   ,  __magic_name__   ,  sample_levels=[2]   ,  save_results=__magic_name__   ,  sample_length=4_0 * model.priors[2].raw_to_tokens				)
       torch.testing.assert_allclose(zs[2][0][:4_0].cpu()   ,  torch.tensor(self.EXPECTED_PRIMED_2				)				)






      @slow
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[Any]          =				JukeboxModel.from_pretrained(self.model_id   ,  min_duration=0				).eval()
       set_seed(0				)
       lowerCamelCase		:     Union[str, Any]          =				torch.rand((1, 5_1_2_0, 1)				)
       with torch.no_grad():
        lowerCamelCase		:     int          =				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():
        lowerCamelCase		:     Optional[Any]          =				model.vqvae.decode(__magic_name__   ,  start_level=2   ,  bs_chunks=x.shape[0]				)
       torch.testing.assert_allclose(x[0, :4_0, 0]   ,  torch.tensor(self.EXPECTED_VQVAE_DECODE				)   ,  atol=1e-4   ,  rtol=1e-4				)





@require_torch
class 							A__		(     unittest.TestCase):
      _UpperCAmelCase    :   List[str]   	=      (JukeboxModel,) if is_torch_available() else ()
      _UpperCAmelCase    :   List[str]   	=      """openai/jukebox-5b-lyrics"""
      _UpperCAmelCase    :   Tuple   	=      {
          """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
      _UpperCAmelCase    :   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
      ]
      _UpperCAmelCase    :   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, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
      ]

      _UpperCAmelCase    :   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
      ]
      _UpperCAmelCase    :   Union[str, 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
      ]

      _UpperCAmelCase    :   Optional[int]   	=      [
          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
      ]
      _UpperCAmelCase    :   Optional[int]   	=      [
          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
      ]

      _UpperCAmelCase    :   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, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
      ]
      _UpperCAmelCase    :   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, 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
      ]

      _UpperCAmelCase    :   str   	=      [
          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
      ]
      _UpperCAmelCase    :   List[str]   	=      [
          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   ,  __magic_name__				):
       lowerCamelCase		:     Dict          =				JukeboxTokenizer.from_pretrained(__magic_name__				)
       lowerCamelCase		:     Optional[int]          =				tokenizer(**self.metas				)["""input_ids"""]
       return tokens
      @slow
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[int]          =				JukeboxModel.from_pretrained(self.model_id   ,  min_duration=0				).eval()
       lowerCamelCase		:     Any          =				self.prepare_inputs(self.model_id				)

       set_seed(0				)
       lowerCamelCase		:     Optional[Any]          =				[torch.zeros(1   ,  0   ,  dtype=torch.long				).cpu() for _ in range(3				)]
       lowerCamelCase		:     List[str]          =				model._sample(__magic_name__   ,  __magic_name__   ,  [0]   ,  sample_length=6_0 * 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				)
       lowerCamelCase		:     int          =				model._sample(__magic_name__   ,  __magic_name__   ,  [1]   ,  sample_length=6_0 * 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				)
       lowerCamelCase		:     Dict          =				model._sample(__magic_name__   ,  __magic_name__   ,  [2]   ,  sample_length=6_0 * 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				):
       lowerCamelCase		:     int          =				JukeboxModel.from_pretrained(self.model_id   ,  min_duration=0				).eval()
       lowerCamelCase		:     Dict          =				[i.cuda() for i in self.prepare_inputs(self.model_id				)]

       set_seed(0				)
       model.priors[0].cuda()
       lowerCamelCase		:     List[Any]          =				[torch.zeros(1   ,  0   ,  dtype=torch.long				).cuda() for _ in range(3				)]
       lowerCamelCase		:     Tuple          =				model._sample(__magic_name__   ,  __magic_name__   ,  [0]   ,  sample_length=6_0 * 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()
       lowerCamelCase		:     Optional[Any]          =				model._sample(__magic_name__   ,  __magic_name__   ,  [1]   ,  sample_length=6_0 * 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()
       lowerCamelCase		:     Optional[int]          =				model._sample(__magic_name__   ,  __magic_name__   ,  [2]   ,  sample_length=6_0 * 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				):
       lowerCamelCase		:     Any          =				"""ArthurZ/jukebox_prior_0"""
       lowerCamelCase		:     List[Any]          =				JukeboxPrior.from_pretrained(__magic_name__   ,  min_duration=0				).eval().half().to("""cuda"""				)

       lowerCamelCase		:     str          =				self.prepare_inputs(__magic_name__				)[0].cuda()
       lowerCamelCase		:     Union[str, Any]          =				model.get_metadata(__magic_name__   ,  0   ,  7_6_8_0   ,  0				)
       set_seed(0				)
       lowerCamelCase		:     int          =				model.sample(1   ,  metadata=__magic_name__   ,  sample_tokens=6_0				)
       self.assertIn(outputs[0].cpu().tolist()   ,  [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]				)






from collections import defaultdict





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     defaultdict[str, int]          =				defaultdict(lowerCamelCase							)
 # Creating a dictionary containing count of each word
 for word in sentence.split():
  occurrence[word] += 1
 return occurrence


if __name__ == "__main__":
      for word, count in word_occurrence("""INPUT STRING""").items():
            print(f'''{word}: {count}''')




