



"""simple docstring"""




import unittest
from unittest import skip

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow
from transformers.trainer_utils import set_seed


if is_torch_available():
     import torch

     from transformers import JukeboxModel, JukeboxPrior, JukeboxTokenizer


@require_torch
class   a__		(						unittest.TestCase     ):
   _lowerCamelCase												=				(JukeboxModel,) if is_torch_available() else ()
   _lowerCamelCase												=				'openai/jukebox-1b-lyrics'
   _lowerCamelCase												=				{
       '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
   _lowerCamelCase												=				[
       1_864, 1_536, 1_213, 1_870, 1_357, 1_536, 519, 880, 1_323, 789, 1_082, 534,
       1_000, 1_445, 1_105, 1_130, 967, 515, 1_434, 1_620, 534, 1_495, 283, 1_445,
       333, 1_307, 539, 1_631, 1_528, 375, 1_434, 673, 627, 710, 778, 1_883,
       1_405, 1_276, 1_455, 1_228
   ]

   _lowerCamelCase												=				[
       1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
       653, 653, 653, 653, 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												=				[
       1_125, 1_751, 697, 1_776, 1_141, 1_476, 391, 697, 1_125, 684, 867, 416,
       844, 1_372, 1_274, 717, 1_274, 844, 1_299, 1_419, 697, 1_370, 317, 1_125,
       191, 1_440, 1_370, 1_440, 1_370, 282, 1_621, 1_370, 368, 349, 867, 1_872,
       1_262, 869, 1_728, 747
   ]
   _lowerCamelCase												=				[
       416, 416, 1_125, 1_125, 416, 416, 416, 416, 416, 416, 416, 416,
       416, 416, 416, 416, 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												=				[
       1_755, 842, 307, 1_843, 1_022, 1_395, 234, 1_554, 806, 739, 1_022, 442,
       616, 556, 268, 1_499, 933, 457, 1_440, 1_837, 755, 985, 308, 902,
       293, 1_443, 1_671, 1_141, 1_533, 555, 1_562, 1_061, 287, 417, 1_022, 2_008,
       1_186, 1_015, 1_777, 268
   ]
   _lowerCamelCase												=				[
       854, 842, 1_353, 114, 1_353, 842, 185, 842, 185, 114, 591, 842,
       185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842,
       591, 842, 1_353, 842, 185, 842, 591, 842, 591, 114, 591, 842,
       185, 842, 591, 89
   ]

   _lowerCamelCase												=				[1_058_304, 0, 786_432, 7_169, 507, 76, 27, 40, 30, 76]

   _lowerCamelCase												=				[
       390, 1_160, 1_002, 1_907, 1_788, 1_788, 1_788, 1_907, 1_002, 1_002, 1_854, 1_002,
       1_002, 1_002, 1_002, 1_002, 1_002, 1_160, 1_160, 1_606, 596, 596, 1_160, 1_002,
       1_516, 596, 1_002, 1_002, 1_002, 1_907, 1_788, 1_788, 1_788, 1_854, 1_788, 1_907,
       1_907, 1_788, 596, 1_626
   ]
   _lowerCamelCase												=				[
       1_236, 1_668, 1_484, 1_920, 1_848, 1_409, 139, 864, 1_828, 1_272, 1_599, 824,
       1_672, 139, 555, 1_484, 824, 1_920, 555, 596, 1_579, 1_599, 1_231, 1_599,
       1_637, 1_407, 212, 824, 1_599, 116, 1_433, 824, 258, 1_599, 1_433, 1_895,
       1_063, 1_433, 1_433, 1_599
   ]
   _lowerCamelCase												=				[
       1_684, 1_873, 1_119, 1_189, 395, 611, 1_901, 972, 890, 1_337, 1_392, 1_927,
       96, 972, 672, 780, 1_119, 890, 158, 771, 1_073, 1_927, 353, 1_331,
       1_269, 1_459, 1_333, 1_645, 812, 1_577, 1_337, 606, 353, 981, 1_466, 619,
       197, 391, 302, 1_930
   ]
   _lowerCamelCase												=				[
       390, 1_160, 1_002, 1_907, 1_788, 1_788, 1_788, 1_907, 1_002, 1_002, 1_854, 1_002,
       1_002, 1_002, 1_002, 1_002, 1_002, 1_160, 1_160, 1_606, 596, 596, 1_160, 1_002,
       1_516, 596, 1_002, 1_002, 1_002, 1_907, 1_788, 1_788, 1_788, 1_854, 1_788, 1_907,
       1_907, 1_788, 596, 1_626
   ]
   _lowerCamelCase												=				[
       -0.0_4_9_2, -0.0_5_2_4, -0.0_5_6_5, -0.0_6_4_0, -0.0_6_8_6, -0.0_6_8_4, -0.0_6_7_7, -0.0_6_6_4,
       -0.0_6_0_5, -0.0_4_9_0, -0.0_3_3_0, -0.0_1_6_8, -0.0_0_8_3, -0.0_0_7_5, -0.0_0_5_1, 0.0_0_2_5,
       0.0_1_3_6, 0.0_2_6_1, 0.0_3_8_6, 0.0_4_9_7, 0.0_5_8_0, 0.0_5_9_9, 0.0_5_8_3, 0.0_6_1_4,
       0.0_7_4_0, 0.0_8_8_9, 0.1_0_2_3, 0.1_1_6_2, 0.1_2_1_1, 0.1_2_1_2, 0.1_2_5_1, 0.1_3_3_6,
       0.1_5_0_2, 0.1_6_8_6, 0.1_8_8_3, 0.2_1_4_8, 0.2_3_6_3, 0.2_4_5_8, 0.2_5_0_7, 0.2_5_3_1
   ]
   _lowerCamelCase												=				[
       0.0_2_5_6, -0.0_5_4_4, 0.1_6_0_0, -0.0_0_3_2, 0.1_0_6_6, 0.0_8_2_5, -0.0_0_1_3, 0.3_4_4_0,
       0.0_2_1_0, 0.0_4_1_2, -0.1_7_7_7, -0.0_8_9_2, -0.0_1_6_4, 0.0_2_8_5, -0.0_6_1_3, -0.0_6_1_7,
       -0.0_1_3_7, -0.0_2_0_1, -0.0_1_7_5, 0.0_2_1_5, -0.0_6_2_7, 0.0_5_2_0, -0.0_7_3_0, 0.0_9_7_0,
       -0.0_1_0_0, 0.0_4_4_2, -0.0_5_8_6, 0.0_2_0_7, -0.0_0_1_5, -0.0_0_8_2
   ]
   _lowerCamelCase												=				[
       0.0_4_1_5, 0.0_8_7_7, 0.0_0_2_2, -0.0_0_5_5, 0.0_7_5_1, 0.0_3_3_4, 0.0_3_2_4, -0.0_0_6_8,
       0.0_0_1_1, 0.0_0_1_7, -0.0_6_7_6, 0.0_6_5_5, -0.0_1_4_3, 0.0_3_9_9, 0.0_3_0_3, 0.0_7_4_3,
       -0.0_1_6_8, -0.0_3_9_4, -0.1_1_1_3, 0.0_1_2_4, 0.0_4_4_2, 0.0_2_6_7, -0.0_0_0_3, -0.1_5_3_6,
       -0.0_1_1_6, -0.1_8_3_7, -0.0_1_8_0, -0.1_0_2_6, -0.0_7_7_7, -0.0_4_5_6
   ]
   _lowerCamelCase												=				[
       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        lowercase				(							self      :							Optional[int]			)    ->       Tuple:
         lowercase  :      Tuple					=   JukeboxTokenizer.from_pretrained(self.model_id			)
         lowercase  :      Dict					=   tokenizer(**self.metas			)['input_ids']
         return tokens


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

         set_seed(0			)
         lowercase  :      Union[str, Any]					=   [torch.zeros(1,     0,     dtype=torch.long			).cpu() for _ in range(3			)]
         lowercase  :      Tuple					=   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			)
         lowercase  :      Union[str, Any]					=   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			)
         lowercase  :      Optional[int]					=   model._sample(lowerCAmelCase,     lowerCAmelCase,     [2],     sample_length=40 * model.priors[2].raw_to_tokens,     save_results=lowerCAmelCase			)
         self.assertIn(zs[2][0].detach().cpu().tolist(),     [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]			)


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

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

         lowercase  :      List[str]					=   model.priors[0]
         lowercase  :      Any					=   0
         lowercase  :      List[Any]					=   top_prior.get_music_tokens_conds(lowerCAmelCase,     start=lowerCAmelCase,     end=start + top_prior.n_ctx			)
         lowercase  :      List[str]					=   top_prior.get_metadata(labels[0].clone(),     lowerCAmelCase,     1058304,     0			)

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

         lowercase	,    lowercase	,    lowercase  :      Optional[Any]					=   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        lowercase				(							self      :							Optional[int]			)    ->       List[str]:
         lowercase  :      Optional[int]					=   False

         lowercase  :      Union[str, Any]					=   JukeboxModel.from_pretrained(self.model_id,     min_duration=0			).eval()
         set_seed(0			)
         lowercase  :      str					=   torch.rand((1, 5120, 1)			)
         lowercase  :      Tuple					=   list(self.prepare_inputs()			)

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

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

         lowercase  :      List[str]					=   torch.cat((zs[1], torch.zeros(1,     2048 - zs[1].shape[-1]			)),     dim=-1			).long()
         lowercase  :      Union[str, Any]					=   [upper_a, upper_a, model.vqvae.encode(lowerCAmelCase,     start_level=0,     bs_chunks=waveform.shape[0]			)[0]]
         lowercase  :      int					=   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        lowercase				(							self      :							List[str]			)    ->       str:
         lowercase  :      Optional[int]					=   JukeboxModel.from_pretrained(self.model_id,     min_duration=0			).eval()
         set_seed(0			)
         lowercase  :      Optional[int]					=   torch.rand((1, 5120, 1)			)
         with torch.no_grad():
               lowercase  :      Union[str, Any]					=   model.vqvae.encode(lowerCAmelCase,     start_level=2,     bs_chunks=x.shape[0]			)
         torch.testing.assert_allclose(zs[0][0],     torch.tensor(self.EXPECTED_VQVAE_ENCODE			)			)

         with torch.no_grad():
               lowercase  :      int					=   model.vqvae.decode(lowerCAmelCase,     start_level=2,     bs_chunks=x.shape[0]			)
         torch.testing.assert_allclose(x[0, :40, 0],     torch.tensor(self.EXPECTED_VQVAE_DECODE			),     atol=1e-4,     rtol=1e-4			)




@require_torch
class   a__		(						unittest.TestCase     ):
   _lowerCamelCase												=				(JukeboxModel,) if is_torch_available() else ()
   _lowerCamelCase												=				'openai/jukebox-5b-lyrics'
   _lowerCamelCase												=				{
       '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
   _lowerCamelCase												=				[
       1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
       653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
       653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
       653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
       1_489, 1_489, 1_489, 1_489, 1_150, 1_853, 1_509, 1_150, 1_357, 1_509, 6, 1_272
   ]
   _lowerCamelCase												=				[
       1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
       653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
       653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
       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												=				[
       1_125, 416, 1_125, 1_125, 1_125, 1_125, 1_125, 416, 416, 416, 416, 416,
       416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
       416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
       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												=				[
       416, 416, 1_125, 1_125, 416, 416, 416, 416, 416, 416, 416, 416, 416,
       416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
       416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 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												=				[
       1_755, 1_061, 234, 1_755, 1_061, 1_755, 185, 290, 307, 307, 616, 616,
       616, 616, 616, 616, 307, 290, 417, 1_755, 234, 1_755, 185, 290,
       290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1_755,
       234, 234, 1_755, 234, 185, 185, 307, 616, 616, 616, 616, 290,
       1_755, 1_755, 1_755, 234, 234, 1_755, 1_572, 290, 307, 616, 34, 616
   ]
   _lowerCamelCase												=				[
       854, 842, 1_353, 114, 1_353, 842, 185, 842, 185, 114, 591, 842, 185,
       417, 185, 842, 307, 842, 591, 842, 185, 842, 185, 842, 591, 842,
       1_353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591,
       89, 591, 842, 591, 842, 591, 417, 1_372, 842, 1_372, 842, 34, 842,
       185, 89, 591, 842, 185, 842, 591, 632
   ]

   _lowerCamelCase												=				[
       1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
       653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
       653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
       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												=				[
       1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
       653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
       653, 653, 653, 653, 653, 653, 653, 1_853, 1_177, 1_536, 1_228,
       710, 475, 1_489, 1_229, 1_224, 231, 1_224, 252, 1_434, 653, 475,
       1_106, 1_877, 1_599, 1_228, 1_600, 1_683, 1_182, 1_853, 475, 1_864,
       252, 1_229, 1_434, 2_001
   ]

   _lowerCamelCase												=				[
       1_125, 1_125, 416, 1_125, 1_125, 416, 1_125, 1_125, 416, 416, 1_125, 416,
       416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
       416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
       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												=				[
       491, 1_755, 34, 1_613, 1_755, 417, 992, 1_613, 222, 842, 1_353, 1_613,
       844, 632, 185, 1_613, 844, 632, 185, 1_613, 185, 842, 677, 1_613,
       185, 114, 1_353, 1_613, 307, 89, 844, 1_613, 307, 1_332, 234, 1_979,
       307, 89, 1_353, 616, 34, 842, 185, 842, 34, 842, 185, 842,
       307, 114, 185, 89, 34, 1_268, 185, 89, 34, 842, 185, 89
   ]


   def        lowercase				(							self      :							Optional[int],     lowerCAmelCase      :							Optional[int]			)    ->       Optional[Any]:
         lowercase  :      Dict					=   JukeboxTokenizer.from_pretrained(lowerCAmelCase			)
         lowercase  :      Optional[int]					=   tokenizer(**self.metas			)['input_ids']
         return tokens


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

         set_seed(0			)
         lowercase  :      Union[str, Any]					=   [torch.zeros(1,     0,     dtype=torch.long			).cpu() for _ in range(3			)]
         lowercase  :      Dict					=   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			)
         lowercase  :      Tuple					=   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			)
         lowercase  :      Tuple					=   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        lowercase				(							self      :							Optional[int]			)    ->       Any:
         lowercase  :      List[str]					=   JukeboxModel.from_pretrained(self.model_id,     min_duration=0			).eval()
         lowercase  :      List[Any]					=   [i.cuda() for i in self.prepare_inputs(self.model_id			)]

         set_seed(0			)
         model.priors[0].cuda()
         lowercase  :      List[str]					=   [torch.zeros(1,     0,     dtype=torch.long			).cuda() for _ in range(3			)]
         lowercase  :      int					=   model._sample(lowerCAmelCase,     lowerCAmelCase,     [0],     sample_length=60 * model.priors[0].raw_to_tokens,     save_results=lowerCAmelCase			)
         torch.testing.assert_allclose(zs[0][0].cpu(),     torch.tensor(self.EXPECTED_GPU_OUTPUTS_2			)			)
         model.priors[0].cpu()

         set_seed(0			)
         model.priors[1].cuda()
         lowercase  :      Optional[int]					=   model._sample(lowerCAmelCase,     lowerCAmelCase,     [1],     sample_length=60 * model.priors[1].raw_to_tokens,     save_results=lowerCAmelCase			)
         torch.testing.assert_allclose(zs[1][0].cpu(),     torch.tensor(self.EXPECTED_GPU_OUTPUTS_1			)			)
         model.priors[1].cpu()

         set_seed(0			)
         model.priors[2].cuda()
         lowercase  :      List[Any]					=   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        lowercase				(							self      :							List[Any]			)    ->       List[Any]:
         lowercase  :      Optional[Any]					=   'ArthurZ/jukebox_prior_0'
         lowercase  :      Any					=   JukeboxPrior.from_pretrained(lowerCAmelCase,     min_duration=0			).eval().half().to('cuda'			)

         lowercase  :      Union[str, Any]					=   self.prepare_inputs(lowerCAmelCase			)[0].cuda()
         lowercase  :      int					=   model.get_metadata(lowerCAmelCase,     0,     7680,     0			)
         set_seed(0			)
         lowercase  :      List[Any]					=   model.sample(1,     metadata=lowerCAmelCase,     sample_tokens=60			)
         self.assertIn(outputs[0].cpu().tolist(),     [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]			)





"""simple docstring"""




import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_UpperCamelCase:				str    							=  logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				42
   _lowerCamelCase												=				None




def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase=0.9_9_9	,      _UpperCAmelCase="cosine"	,      )     ->			Optional[Any]:






      '''simple docstring'''


      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_UpperCAmelCase				):
                  return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(_UpperCAmelCase				):
                  return math.exp(t * -1_2.0				)

      else:
            raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''				)

      lowercase  :      Any					=   []
      for i in range(_UpperCAmelCase				):
            lowercase  :      List[str]					=   i / num_diffusion_timesteps
            lowercase  :      str					=   (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase				) / alpha_bar_fn(_UpperCAmelCase				)	,      _UpperCAmelCase				)				)
      return torch.tensor(_UpperCAmelCase	,      dtype=torch.floataa				)





class   a__		(						SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				[e.name for e in KarrasDiffusionSchedulers]
   _lowerCamelCase												=				1


   @register_to_config
   def __init__(							self      :							str,     lowerCAmelCase      :							int = 1000,     lowerCAmelCase      :							float = 0.0001,     lowerCAmelCase      :							float = 0.02,     lowerCAmelCase      :							str = "linear",     lowerCAmelCase      :							Optional[Union[np.ndarray, List[float]]] = None,     lowerCAmelCase      :							str = "epsilon",     lowerCAmelCase      :							str = "linspace",     lowerCAmelCase      :							int = 0,     )    ->       List[Any]:
         if trained_betas is not None:
               lowercase  :      Union[str, Any]					=   torch.tensor(lowerCAmelCase,     dtype=torch.floataa			)
         elif beta_schedule == "linear":
               lowercase  :      Union[str, Any]					=   torch.linspace(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase,     dtype=torch.floataa			)
         elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               lowercase  :      str					=   (
                   torch.linspace(beta_start**0.5,     beta_end**0.5,     lowerCAmelCase,     dtype=torch.floataa			) ** 2
               )
         elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               lowercase  :      List[Any]					=   betas_for_alpha_bar(lowerCAmelCase			)
         else:
               raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''			)

         lowercase  :      Any					=   1.0 - self.betas
         lowercase  :      Optional[int]					=   torch.cumprod(self.alphas,     dim=0			)

         lowercase  :      Optional[Any]					=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
         lowercase  :      Optional[int]					=   np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
         lowercase  :      int					=   torch.from_numpy(lowerCAmelCase			)

         # setable values
         lowercase  :      List[str]					=   None
         lowercase  :      Tuple					=   np.linspace(0,     num_train_timesteps - 1,     lowerCAmelCase,     dtype=lowerCAmelCase			)[::-1].copy()
         lowercase  :      Dict					=   torch.from_numpy(lowerCAmelCase			)
         lowercase  :      str					=   False


   @property
   def        lowercase				(							self      :							Tuple			)    ->       Tuple:
         # standard deviation of the initial noise distribution
         if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5


   def        lowercase				(							self      :							Any,     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							Union[float, torch.FloatTensor]			)    ->       torch.FloatTensor:
         if isinstance(lowerCAmelCase,     torch.Tensor			):
               lowercase  :      Any					=   timestep.to(self.timesteps.device			)
         lowercase  :      List[Any]					=   (self.timesteps == timestep).nonzero().item()
         lowercase  :      Union[str, Any]					=   self.sigmas[step_index]
         lowercase  :      Dict					=   sample / ((sigma**2 + 1) ** 0.5)
         lowercase  :      Union[str, Any]					=   True
         return sample


   def        lowercase				(							self      :							Tuple,     lowerCAmelCase      :							int,     lowerCAmelCase      :							Union[str, torch.device] = None			)    ->       List[Any]:
         lowercase  :      Union[str, Any]					=   num_inference_steps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
               lowercase  :      int					=   np.linspace(0,     self.config.num_train_timesteps - 1,     lowerCAmelCase,     dtype=lowerCAmelCase			)[
                   ::-1
               ].copy()
         elif self.config.timestep_spacing == "leading":
               lowercase  :      List[str]					=   self.config.num_train_timesteps // self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               lowercase  :      Any					=   (np.arange(0,     lowerCAmelCase			) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase			)
               timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
               lowercase  :      Optional[Any]					=   self.config.num_train_timesteps / self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               lowercase  :      Optional[int]					=   (np.arange(self.config.num_train_timesteps,     0,     -step_ratio			)).round().copy().astype(lowerCAmelCase			)
               timesteps -= 1
         else:
               raise ValueError(
                   f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''			)

         lowercase  :      int					=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
         lowercase  :      List[str]					=   np.interp(lowerCAmelCase,     np.arange(0,     len(lowerCAmelCase			)			),     lowerCAmelCase			)
         lowercase  :      Any					=   np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
         lowercase  :      Optional[Any]					=   torch.from_numpy(lowerCAmelCase			).to(device=lowerCAmelCase			)
         if str(lowerCAmelCase			).startswith('mps'			):
               # mps does not support float64
               lowercase  :      List[Any]					=   torch.from_numpy(lowerCAmelCase			).to(lowerCAmelCase,     dtype=torch.floataa			)
         else:
               lowercase  :      List[str]					=   torch.from_numpy(lowerCAmelCase			).to(device=lowerCAmelCase			)


   def        lowercase				(							self      :							Optional[Any],     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							Union[float, torch.FloatTensor],     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							Optional[torch.Generator] = None,     lowerCAmelCase      :							bool = True,     )    ->       Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

         if (
             isinstance(lowerCAmelCase,     lowerCAmelCase			)
             or isinstance(lowerCAmelCase,     torch.IntTensor			)
             or isinstance(lowerCAmelCase,     torch.LongTensor			)
         ):
               raise ValueError(
                   (
                       'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                       ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
                       ' one of the `scheduler.timesteps` as a timestep.'
                   ),     )

         if not self.is_scale_input_called:
               logger.warning(
                   'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
                   'See `StableDiffusionPipeline` for a usage example.'			)

         if isinstance(lowerCAmelCase,     torch.Tensor			):
               lowercase  :      List[str]					=   timestep.to(self.timesteps.device			)

         lowercase  :      List[str]					=   (self.timesteps == timestep).nonzero().item()
         lowercase  :      Optional[int]					=   self.sigmas[step_index]

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
               lowercase  :      Optional[Any]					=   sample - sigma * model_output
         elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               lowercase  :      int					=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
         elif self.config.prediction_type == "sample":
               raise NotImplementedError('prediction_type not implemented yet: sample'			)
         else:
               raise ValueError(
                   f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''			)

         lowercase  :      List[Any]					=   self.sigmas[step_index]
         lowercase  :      str					=   self.sigmas[step_index + 1]
         lowercase  :      Optional[int]					=   (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
         lowercase  :      Optional[int]					=   (sigma_to**2 - sigma_up**2) ** 0.5

         # 2. Convert to an ODE derivative
         lowercase  :      Tuple					=   (sample - pred_original_sample) / sigma

         lowercase  :      str					=   sigma_down - sigma

         lowercase  :      int					=   sample + derivative * dt

         lowercase  :      Dict					=   model_output.device
         lowercase  :      List[Any]					=   randn_tensor(model_output.shape,     dtype=model_output.dtype,     device=lowerCAmelCase,     generator=lowerCAmelCase			)

         lowercase  :      List[str]					=   prev_sample + noise * sigma_up

         if not return_dict:
               return (prev_sample,)

         return EulerAncestralDiscreteSchedulerOutput(
             prev_sample=lowerCAmelCase,     pred_original_sample=lowerCAmelCase			)


   def        lowercase				(							self      :							int,     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							torch.FloatTensor,     )    ->       torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         lowercase  :      Tuple					=   self.sigmas.to(device=original_samples.device,     dtype=original_samples.dtype			)
         if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase			):
               # mps does not support float64
               lowercase  :      Dict					=   self.timesteps.to(original_samples.device,     dtype=torch.floataa			)
               lowercase  :      Union[str, Any]					=   timesteps.to(original_samples.device,     dtype=torch.floataa			)
         else:
               lowercase  :      List[str]					=   self.timesteps.to(original_samples.device			)
               lowercase  :      Tuple					=   timesteps.to(original_samples.device			)

         lowercase  :      Union[str, Any]					=   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

         lowercase  :      Union[str, Any]					=   sigmas[step_indices].flatten()
         while len(sigma.shape			) < len(original_samples.shape			):
               lowercase  :      Dict					=   sigma.unsqueeze(-1			)

         lowercase  :      Optional[Any]					=   original_samples + noise * sigma
         return noisy_samples






   def __len__(							self      :							int			)    ->       List[str]:
         return self.config.num_train_timesteps
