



"""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]      )     ->							Optional[Any]:
         lowercase	:   Optional[int]											= JukeboxTokenizer.from_pretrained(self.model_id      )
         lowercase	:   Optional[int]											= tokenizer(**self.metas      )['input_ids']
         return tokens


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

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

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

         lowercase	:   Any											= model.priors[0]
         lowercase	:   Tuple											= 0
         lowercase	:   int											= top_prior.get_music_tokens_conds(_lowerCAmelCase,  start=_lowerCAmelCase,  end=start + top_prior.n_ctx      )
         lowercase	:   Dict											= 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	:   Tuple											= 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[Any]      )     ->							Optional[int]:
         lowercase	:   str											= False

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

         lowercase	:   List[Any]											= [model.vqvae.encode(_lowerCAmelCase,  start_level=2,  bs_chunks=waveform.shape[0]      )[0], None, None]
         lowercase	:   str											= 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	:   Optional[Any]											= torch.cat((zs[0], torch.zeros(1,  2048 - zs[0].shape[-1]      )),  dim=-1      ).long()
         lowercase	:   str											= [upper_a, model.vqvae.encode(_lowerCAmelCase,  start_level=1,  bs_chunks=waveform.shape[0]      )[0], None]
         lowercase	:   str											= 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	:   Optional[Any]											= torch.cat((zs[1], torch.zeros(1,  2048 - zs[1].shape[-1]      )),  dim=-1      ).long()
         lowercase	:   Dict											= [upper_a, upper_a, model.vqvae.encode(_lowerCAmelCase,  start_level=0,  bs_chunks=waveform.shape[0]      )[0]]
         lowercase	:   Any											= 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]      )     ->							Any:
         lowercase	:   Optional[Any]											= JukeboxModel.from_pretrained(self.model_id,  min_duration=0      ).eval()
         set_seed(0      )
         lowercase	:   str											= torch.rand((1, 5120, 1)      )
         with torch.no_grad():
               lowercase	:   Tuple											= 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	:   Tuple											= 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   :				List[Any],  lowerCAmelCase   :				Optional[int]      )     ->							Tuple:
         lowercase	:   Any											= JukeboxTokenizer.from_pretrained(_lowerCAmelCase      )
         lowercase	:   Union[str, Any]											= tokenizer(**self.metas      )['input_ids']
         return tokens


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

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

         set_seed(0      )
         model.priors[0].cuda()
         lowercase	:   Dict											= [torch.zeros(1,  0,  dtype=torch.long      ).cuda() for _ in range(3      )]
         lowercase	:   List[str]											= 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	:   List[Any]											= 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	:   Dict											= 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   :				Optional[int]      )     ->							List[Any]:
         lowercase	:   List[Any]											= 'ArthurZ/jukebox_prior_0'
         lowercase	:   Tuple											= JukeboxPrior.from_pretrained(_lowerCAmelCase,  min_duration=0      ).eval().half().to('cuda'      )

         lowercase	:   str											= self.prepare_inputs(_lowerCAmelCase      )[0].cuda()
         lowercase	:   str											= 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]      )


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCamelCase:					str     =logging.get_logger(__name__)

_UpperCamelCase:					Optional[Any]     ={
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}





class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       UpperCamelCase_				 =							'''oneformer'''
       UpperCamelCase_				 =							{'''hidden_size''': '''hidden_dim'''}



       def __init__(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			Optional[Dict] = None  ,      _lowerCAmelCase					:			int = 255  ,      _lowerCAmelCase					:			int = 150  ,      _lowerCAmelCase					:			int = 0.1  ,      _lowerCAmelCase					:			float = 2.0  ,      _lowerCAmelCase					:			float = 5.0  ,      _lowerCAmelCase					:			float = 5.0  ,      _lowerCAmelCase					:			float = 0.5  ,      _lowerCAmelCase					:			float = 0.07  ,      _lowerCAmelCase					:			int = 1_2544  ,      _lowerCAmelCase					:			float = 3.0  ,      _lowerCAmelCase					:			float = 0.75  ,      _lowerCAmelCase					:			float = 0.02  ,      _lowerCAmelCase					:			float = 1.0  ,      _lowerCAmelCase					:			float = 1e-05  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			Optional[list] = [4, 8, 16, 32]  ,      _lowerCAmelCase					:			int = 77  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 77  ,      _lowerCAmelCase					:			int = 6  ,      _lowerCAmelCase					:			int = 4_9408  ,      _lowerCAmelCase					:			int = 2  ,      _lowerCAmelCase					:			int = 16  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 1024  ,      _lowerCAmelCase					:			str = "GN"  ,      _lowerCAmelCase					:			int = 6  ,      _lowerCAmelCase					:			int = 10  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			int = 8  ,      _lowerCAmelCase					:			float = 0.1  ,      _lowerCAmelCase					:			int = 2048  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			int = 2  ,      _lowerCAmelCase					:			int = 4  ,      **_lowerCAmelCase					:			Any  ,      )					->   Tuple:
         if backbone_config is None:
           logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'       )
           _lowerCAmelCase						=		CONFIG_MAPPING['swin'](
               image_size=224  ,      in_channels=3  ,      patch_size=4  ,      embed_dim=96  ,      depths=[2, 2, 6, 2]  ,      num_heads=[3, 6, 12, 24]  ,      window_size=7  ,      drop_path_rate=0.3  ,      use_absolute_embeddings=_lowerCAmelCase  ,      out_features=['stage1', 'stage2', 'stage3', 'stage4']  ,      )
         elif isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           _lowerCAmelCase						=		backbone_config.get('model_type'       )
           _lowerCAmelCase						=		CONFIG_MAPPING[backbone_model_type]
           _lowerCAmelCase						=		config_class.from_dict(_lowerCAmelCase       )

         _lowerCAmelCase						=		backbone_config

         _lowerCAmelCase						=		ignore_value
         _lowerCAmelCase						=		num_queries
         _lowerCAmelCase						=		no_object_weight
         _lowerCAmelCase						=		class_weight
         _lowerCAmelCase						=		mask_weight
         _lowerCAmelCase						=		dice_weight
         _lowerCAmelCase						=		contrastive_weight
         _lowerCAmelCase						=		contrastive_temperature
         _lowerCAmelCase						=		train_num_points
         _lowerCAmelCase						=		oversample_ratio
         _lowerCAmelCase						=		importance_sample_ratio
         _lowerCAmelCase						=		init_std
         _lowerCAmelCase						=		init_xavier_std
         _lowerCAmelCase						=		layer_norm_eps
         _lowerCAmelCase						=		is_training
         _lowerCAmelCase						=		use_auxiliary_loss
         _lowerCAmelCase						=		output_auxiliary_logits
         _lowerCAmelCase						=		strides
         _lowerCAmelCase						=		task_seq_len
         _lowerCAmelCase						=		text_encoder_width
         _lowerCAmelCase						=		text_encoder_context_length
         _lowerCAmelCase						=		text_encoder_num_layers
         _lowerCAmelCase						=		text_encoder_vocab_size
         _lowerCAmelCase						=		text_encoder_proj_layers
         _lowerCAmelCase						=		text_encoder_n_ctx
         _lowerCAmelCase						=		conv_dim
         _lowerCAmelCase						=		mask_dim
         _lowerCAmelCase						=		hidden_dim
         _lowerCAmelCase						=		encoder_feedforward_dim
         _lowerCAmelCase						=		norm
         _lowerCAmelCase						=		encoder_layers
         _lowerCAmelCase						=		decoder_layers
         _lowerCAmelCase						=		use_task_norm
         _lowerCAmelCase						=		num_attention_heads
         _lowerCAmelCase						=		dropout
         _lowerCAmelCase						=		dim_feedforward
         _lowerCAmelCase						=		pre_norm
         _lowerCAmelCase						=		enforce_input_proj
         _lowerCAmelCase						=		query_dec_layers
         _lowerCAmelCase						=		common_stride
         _lowerCAmelCase						=		decoder_layers

         super().__init__(**_lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Dict[str, any]:
         _lowerCAmelCase						=		copy.deepcopy(self.__dict__       )
         _lowerCAmelCase						=		self.backbone_config.to_dict()
         _lowerCAmelCase						=		self.__class__.model_type
         return output
