






'''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						):
     '''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,\n    Who said "Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ',
     }
     # fmt: off
     _snake_case             =  [
         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
     ]

     _snake_case             =  [
         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
     ]

     _snake_case             =  [
         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
     ]
     _snake_case             =  [
         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
     ]

     _snake_case             =  [
         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
     ]
     _snake_case             =  [
         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
     ]

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

     _snake_case             =  [
         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
     ]
     _snake_case             =  [
         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
     ]
     _snake_case             =  [
         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
     ]
     _snake_case             =  [
         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
     ]
     _snake_case             =  [
         -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
     ]
     _snake_case             =  [
         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
     ]
     _snake_case             =  [
         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
     ]
     _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     _UpperCamelCase   (self    :	Dict					)  ->       int:
           __A		   =      JukeboxTokenizer.from_pretrained(self.model_id					)
           __A		   =      tokenizer(**self.metas					)["input_ids"]
           return tokens






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

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

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

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






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

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

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

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

           __A   ,				__A   ,				__A		   =      top_prior.get_cond(lowercase__				,				lowercase__					)
           torch.testing.assert_allclose(
               audio_conditioning[0][0][:30].detach()				,				torch.tensor(self.EXPECTED_AUDIO_COND					)				,				atol=1e-4				,				rtol=1e-4					)
           torch.testing.assert_allclose(
               metadata_conditioning[0][0][:30].detach()				,				torch.tensor(self.EXPECTED_META_COND					)				,				atol=1e-4				,				rtol=1e-4					)
           torch.testing.assert_allclose(
               lyric_tokens[0, :30].detach()				,				torch.tensor(self.EXPECTED_LYRIC_COND					)				,				atol=1e-4				,				rtol=1e-4					)






     @slow
     def     _UpperCamelCase   (self    :	str					)  ->       str:
           __A		   =      False

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

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

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

           __A		   =      torch.cat((zs[1], torch.zeros(1				,				2048 - zs[1].shape[-1]					))				,				dim=-1					).long()
           __A		   =      [upper_a, upper_a, model.vqvae.encode(lowercase__				,				start_level=0				,				bs_chunks=waveform.shape[0]					)[0]]
           __A		   =      model._sample(
               lowercase__				,				lowercase__				,				sample_levels=[2]				,				save_results=lowercase__				,				sample_length=40 * model.priors[2].raw_to_tokens					)
           torch.testing.assert_allclose(zs[2][0][:40].cpu()				,				torch.tensor(self.EXPECTED_PRIMED_2					)					)






     @slow
     def     _UpperCamelCase   (self    :	Tuple					)  ->       Union[str, Any]:
           __A		   =      JukeboxModel.from_pretrained(self.model_id				,				min_duration=0					).eval()
           set_seed(0					)
           __A		   =      torch.rand((1, 5120, 1)					)
           with torch.no_grad():
                 __A		   =      model.vqvae.encode(lowercase__				,				start_level=2				,				bs_chunks=x.shape[0]					)
           torch.testing.assert_allclose(zs[0][0]				,				torch.tensor(self.EXPECTED_VQVAE_ENCODE					)					)

           with torch.no_grad():
                 __A		   =      model.vqvae.decode(lowercase__				,				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						):
     '''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,\n    Who said "Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ',
     }

     # fmt: off
     _snake_case             =  [
         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
     ]
     _snake_case             =  [
         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
     ]

     _snake_case             =  [
         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
     ]
     _snake_case             =  [
         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
     ]

     _snake_case             =  [
         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
     ]
     _snake_case             =  [
         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
     ]

     _snake_case             =  [
         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
     ]
     _snake_case             =  [
         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
     ]

     _snake_case             =  [
         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
     ]
     _snake_case             =  [
         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     _UpperCamelCase   (self    :	Tuple				,				lowercase__    :	Tuple					)  ->       List[Any]:
           __A		   =      JukeboxTokenizer.from_pretrained(lowercase__					)
           __A		   =      tokenizer(**self.metas					)["input_ids"]
           return tokens






     @slow
     def     _UpperCamelCase   (self    :	Any					)  ->       List[Any]:
           __A		   =      JukeboxModel.from_pretrained(self.model_id				,				min_duration=0					).eval()
           __A		   =      self.prepare_inputs(self.model_id					)

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

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

           set_seed(0					)
           __A		   =      model._sample(lowercase__				,				lowercase__				,				[2]				,				sample_length=60 * model.priors[2].raw_to_tokens				,				save_results=lowercase__					)
           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    :	Union[str, Any]					)  ->       Any:
           __A		   =      JukeboxModel.from_pretrained(self.model_id				,				min_duration=0					).eval()
           __A		   =      [i.cuda() for i in self.prepare_inputs(self.model_id					)]

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

           set_seed(0					)
           model.priors[1].cuda()
           __A		   =      model._sample(lowercase__				,				lowercase__				,				[1]				,				sample_length=60 * model.priors[1].raw_to_tokens				,				save_results=lowercase__					)
           torch.testing.assert_allclose(zs[1][0].cpu()				,				torch.tensor(self.EXPECTED_GPU_OUTPUTS_1					)					)
           model.priors[1].cpu()

           set_seed(0					)
           model.priors[2].cuda()
           __A		   =      model._sample(lowercase__				,				lowercase__				,				[2]				,				sample_length=60 * model.priors[2].raw_to_tokens				,				save_results=lowercase__					)
           torch.testing.assert_allclose(zs[2][0].cpu()				,				torch.tensor(self.EXPECTED_GPU_OUTPUTS_0					)					)






     @slow
     def     _UpperCamelCase   (self    :	str					)  ->       Any:
           __A		   =      "ArthurZ/jukebox_prior_0"
           __A		   =      JukeboxPrior.from_pretrained(lowercase__				,				min_duration=0					).eval().half().to("cuda"					)

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










'''simple docstring'''







import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType






@require_torch
class 					a__    :
     '''simple docstring'''







     _snake_case             =  ()
     _snake_case             =  True






     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Union[str, Any]:
           __A		   =      self.config_class

           # test default config
           __A		   =      config_class()
           self.assertIsNotNone(lowercase__					)
           __A		   =      ["stem"] + [f"stage{idx}" for idx in range(1				,				len(config.depths					) + 1					)]
           self.assertEqual(config.stage_names				,				lowercase__					)
           self.assertTrue(set(config.out_features					).issubset(set(config.stage_names					)					)					)

           # Test out_features and out_indices are correctly set
           # out_features and out_indices both None
           __A		   =      config_class(out_features=lowercase__				,				out_indices=lowercase__					)
           self.assertEqual(config.out_features				,				[config.stage_names[-1]]					)
           self.assertEqual(config.out_indices				,				[len(config.stage_names					) - 1]					)

           # out_features and out_indices both set
           __A		   =      config_class(out_features=["stem", "stage1"]				,				out_indices=[0, 1]					)
           self.assertEqual(config.out_features				,				["stem", "stage1"]					)
           self.assertEqual(config.out_indices				,				[0, 1]					)

           # Only out_features set
           __A		   =      config_class(out_features=["stage1", "stage3"]					)
           self.assertEqual(config.out_features				,				["stage1", "stage3"]					)
           self.assertEqual(config.out_indices				,				[1, 3]					)

           # Only out_indices set
           __A		   =      config_class(out_indices=[0, 2]					)
           self.assertEqual(config.out_features				,				[config.stage_names[0], config.stage_names[2]]					)
           self.assertEqual(config.out_indices				,				[0, 2]					)

           # Error raised when out_indices do not correspond to out_features
           with self.assertRaises(lowercase__					):
                 __A		   =      config_class(out_features=["stage1", "stage2"]				,				out_indices=[0, 2]					)






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Optional[Any]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 __A		   =      model_class(lowercase__					)
                 __A		   =      inspect.signature(model.forward					)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 __A		   =      [*signature.parameters.keys()]
                 __A		   =      ["pixel_values"]
                 self.assertListEqual(arg_names[:1]				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Dict					)  ->       Union[str, Any]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 __A		   =      model_class(lowercase__					)
                 self.assertEqual(len(model.channels					)				,				len(config.out_features					)					)
                 __A		   =      model.num_features
                 __A		   =      [config.stage_names.index(lowercase__					) for feat in config.out_features]
                 __A		   =      [num_features[idx] for idx in out_indices]
                 self.assertListEqual(model.channels				,				lowercase__					)

                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      None
                 __A		   =      model_class(lowercase__					)
                 self.assertEqual(len(model.channels					)				,				1					)
                 self.assertListEqual(model.channels				,				[num_features[-1]]					)

                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      None
                 __A		   =      model_class(lowercase__					)
                 self.assertEqual(len(model.channels					)				,				1					)
                 self.assertListEqual(model.channels				,				[num_features[-1]]					)






     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Optional[Any]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.eval()
                 __A		   =      model(**lowercase__					)

                 self.assertEqual(len(result.feature_maps					)				,				len(config.out_features					)					)
                 self.assertEqual(len(model.channels					)				,				len(config.out_features					)					)
                 self.assertEqual(len(result.feature_maps					)				,				len(config.out_indices					)					)
                 self.assertEqual(len(model.channels					)				,				len(config.out_indices					)					)

                 # Check output of last stage is taken if out_features=None, out_indices=None
                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      None
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.eval()
                 __A		   =      model(**lowercase__					)

                 self.assertEqual(len(result.feature_maps					)				,				1					)
                 self.assertEqual(len(model.channels					)				,				1					)

                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      None
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.eval()
                 __A		   =      model(**lowercase__					)

                 self.assertEqual(len(result.feature_maps					)				,				1					)
                 self.assertEqual(len(model.channels					)				,				1					)

                 # Check backbone can be initialized with fresh weights
                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      False
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.eval()
                 __A		   =      model(**lowercase__					)






     def     _UpperCamelCase   (self    :	Dict					)  ->       Optional[int]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

           for backbone_class in self.all_model_classes:
                 __A		   =      backbone_class(lowercase__					)

                 self.assertTrue(hasattr(lowercase__				,				"backbone_type"					)					)
                 self.assertTrue(hasattr(lowercase__				,				"stage_names"					)					)
                 self.assertTrue(hasattr(lowercase__				,				"num_features"					)					)
                 self.assertTrue(hasattr(lowercase__				,				"out_indices"					)					)
                 self.assertTrue(hasattr(lowercase__				,				"out_features"					)					)
                 self.assertTrue(hasattr(lowercase__				,				"out_feature_channels"					)					)
                 self.assertTrue(hasattr(lowercase__				,				"channels"					)					)

                 self.assertIsInstance(backbone.backbone_type				,				lowercase__					)
                 # Verify num_features has been initialized in the backbone init
                 self.assertIsNotNone(backbone.num_features					)
                 self.assertTrue(len(backbone.channels					) == len(backbone.out_indices					)					)
                 self.assertTrue(len(backbone.stage_names					) == len(backbone.num_features					)					)
                 self.assertTrue(len(backbone.channels					) <= len(backbone.num_features					)					)
                 self.assertTrue(len(backbone.out_feature_channels					) == len(backbone.stage_names					)					)






     def     _UpperCamelCase   (self    :	Tuple					)  ->       int:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()
           __A		   =      inputs_dict["pixel_values"].shape[0]

           for backbone_class in self.all_model_classes:
                 __A		   =      backbone_class(lowercase__					)
                 backbone.to(lowercase__					)
                 backbone.eval()

                 __A		   =      backbone(**lowercase__					)

                 # Test default outputs and verify feature maps
                 self.assertIsInstance(outputs.feature_maps				,				lowercase__					)
                 self.assertTrue(len(outputs.feature_maps					) == len(backbone.channels					)					)
                 for feature_map, n_channels in zip(outputs.feature_maps				,				backbone.channels					):
                       self.assertTrue(feature_map.shape[:2]				,				(batch_size, n_channels)					)
                 self.assertIsNone(outputs.hidden_states					)
                 self.assertIsNone(outputs.attentions					)

                 # Test output_hidden_states=True
                 __A		   =      backbone(**lowercase__				,				output_hidden_states=lowercase__					)
                 self.assertIsNotNone(outputs.hidden_states					)
                 self.assertTrue(len(outputs.hidden_states					)				,				len(backbone.stage_names					)					)
                 for hidden_state, n_channels in zip(outputs.hidden_states				,				backbone.channels					):
                       self.assertTrue(hidden_state.shape[:2]				,				(batch_size, n_channels)					)

                 # Test output_attentions=True
                 if self.has_attentions:
                       __A		   =      backbone(**lowercase__				,				output_attentions=lowercase__					)
                       self.assertIsNotNone(outputs.attentions					)


