
'''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 						__snake_case (						unittest.TestCase  ):
		a_        =				(JukeboxModel,) if is_torch_available() else ()
		a_        =				"openai/jukebox-1b-lyrics"
		a_        =				{
		    "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
		a_        =				[
		    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
		]

		a_        =				[
		    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
		]

		a_        =				[
		    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
		]
		a_        =				[
		    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
		]

		a_        =				[
		    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
		]
		a_        =				[
		    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
		]

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

		a_        =				[
		    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
		]
		a_        =				[
		    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
		]
		a_        =				[
		    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
		]
		a_        =				[
		    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
		]
		a_        =				[
		    -0.04_92, -0.05_24, -0.05_65, -0.06_40, -0.06_86, -0.06_84, -0.06_77, -0.06_64,
		    -0.06_05, -0.04_90, -0.03_30, -0.01_68, -0.00_83, -0.00_75, -0.00_51, 0.00_25,
		    0.01_36, 0.02_61, 0.03_86, 0.04_97, 0.05_80, 0.05_99, 0.05_83, 0.06_14,
		    0.07_40, 0.08_89, 0.10_23, 0.11_62, 0.12_11, 0.12_12, 0.12_51, 0.13_36,
		    0.15_02, 0.16_86, 0.18_83, 0.21_48, 0.23_63, 0.24_58, 0.25_07, 0.25_31
		]
		a_        =				[
		    0.02_56, -0.05_44, 0.16_00, -0.00_32, 0.10_66, 0.08_25, -0.00_13, 0.34_40,
		    0.02_10, 0.04_12, -0.17_77, -0.08_92, -0.01_64, 0.02_85, -0.06_13, -0.06_17,
		    -0.01_37, -0.02_01, -0.01_75, 0.02_15, -0.06_27, 0.05_20, -0.07_30, 0.09_70,
		    -0.01_00, 0.04_42, -0.05_86, 0.02_07, -0.00_15, -0.00_82
		]
		a_        =				[
		    0.04_15, 0.08_77, 0.00_22, -0.00_55, 0.07_51, 0.03_34, 0.03_24, -0.00_68,
		    0.00_11, 0.00_17, -0.06_76, 0.06_55, -0.01_43, 0.03_99, 0.03_03, 0.07_43,
		    -0.01_68, -0.03_94, -0.11_13, 0.01_24, 0.04_42, 0.02_67, -0.00_03, -0.15_36,
		    -0.01_16, -0.18_37, -0.01_80, -0.10_26, -0.07_77, -0.04_56
		]
		a_        =				[
		    76, 27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45, 45, 76, 38, 31, 33,
		    45, 76, 41, 32, 76, 45, 46, 41, 40, 31, 78, 76
		]




		def   _snake_case  (     self				:Dict)				-> List[str]:






							'''simple docstring'''






							UpperCamelCase_    :  List[str]				    =    JukeboxTokenizer.from_pretrained(self.model_id)
							UpperCamelCase_    :  Optional[int]				    =    tokenizer(**self.metas)["""input_ids"""]
							return tokens




		@slow
		def   _snake_case  (     self				:Union[str, Any])				-> List[str]:






							'''simple docstring'''






							UpperCamelCase_    :  List[str]				    =    JukeboxModel.from_pretrained(self.model_id ,						min_duration=0).eval()
							UpperCamelCase_    :  Optional[int]				    =    self.prepare_inputs()

							set_seed(0)
							UpperCamelCase_    :  str				    =    [torch.zeros(1 ,						0 ,						dtype=torch.long).cpu() for _ in range(3)]
							UpperCamelCase_    :  int				    =    model._sample(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						[0] ,						sample_length=4_0 * model.priors[0].raw_to_tokens ,						save_results=_SCREAMING_SNAKE_CASE)
							self.assertIn(zs[0][0].detach().cpu().tolist() ,						[self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2])

							set_seed(0)
							UpperCamelCase_    :  Optional[Any]				    =    model._sample(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						[1] ,						sample_length=4_0 * model.priors[1].raw_to_tokens ,						save_results=_SCREAMING_SNAKE_CASE)
							self.assertIn(zs[1][0].detach().cpu().tolist() ,						[self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2])

							set_seed(0)
							UpperCamelCase_    :  Tuple				    =    model._sample(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						[2] ,						sample_length=4_0 * model.priors[2].raw_to_tokens ,						save_results=_SCREAMING_SNAKE_CASE)
							self.assertIn(zs[2][0].detach().cpu().tolist() ,						[self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2])




		@slow
		def   _snake_case  (     self				:Union[str, Any])				-> Tuple:






							'''simple docstring'''






							UpperCamelCase_    :  List[Any]				    =    False
							UpperCamelCase_    :  Any				    =    JukeboxModel.from_pretrained(self.model_id ,						min_duration=0).eval()

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

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

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

							UpperCamelCase_    ,UpperCamelCase_    ,UpperCamelCase_    :  int				    =    top_prior.get_cond(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE)
							torch.testing.assert_allclose(
							    audio_conditioning[0][0][:3_0].detach() ,						torch.tensor(self.EXPECTED_AUDIO_COND) ,						atol=1E-4 ,						rtol=1E-4)
							torch.testing.assert_allclose(
							    metadata_conditioning[0][0][:3_0].detach() ,						torch.tensor(self.EXPECTED_META_COND) ,						atol=1E-4 ,						rtol=1E-4)
							torch.testing.assert_allclose(
							    lyric_tokens[0, :3_0].detach() ,						torch.tensor(self.EXPECTED_LYRIC_COND) ,						atol=1E-4 ,						rtol=1E-4)




		@slow
		def   _snake_case  (     self				:str)				-> Optional[int]:






							'''simple docstring'''






							UpperCamelCase_    :  Optional[Any]				    =    False

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

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

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

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





		@slow
		def   _snake_case  (     self				:List[str])				-> str:






							'''simple docstring'''






							UpperCamelCase_    :  Tuple				    =    JukeboxModel.from_pretrained(self.model_id ,						min_duration=0).eval()
							set_seed(0)
							UpperCamelCase_    :  List[str]				    =    torch.rand((1, 5_1_2_0, 1))
							with torch.no_grad():
												UpperCamelCase_    :  Optional[Any]				    =    model.vqvae.encode(_SCREAMING_SNAKE_CASE ,						start_level=2 ,						bs_chunks=x.shape[0])
							torch.testing.assert_allclose(zs[0][0] ,						torch.tensor(self.EXPECTED_VQVAE_ENCODE))

							with torch.no_grad():
												UpperCamelCase_    :  List[Any]				    =    model.vqvae.decode(_SCREAMING_SNAKE_CASE ,						start_level=2 ,						bs_chunks=x.shape[0])
							torch.testing.assert_allclose(x[0, :4_0, 0] ,						torch.tensor(self.EXPECTED_VQVAE_DECODE) ,						atol=1E-4 ,						rtol=1E-4)







@require_torch
class 						__snake_case (						unittest.TestCase  ):
		a_        =				(JukeboxModel,) if is_torch_available() else ()
		a_        =				"openai/jukebox-5b-lyrics"
		a_        =				{
		    "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
		a_        =				[
		    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
		]
		a_        =				[
		    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
		]

		a_        =				[
		    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
		]
		a_        =				[
		    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
		]

		a_        =				[
		    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
		]
		a_        =				[
		    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
		]

		a_        =				[
		    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
		]
		a_        =				[
		    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
		]

		a_        =				[
		    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
		]
		a_        =				[
		    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   _snake_case  (     self				:Any ,						_SCREAMING_SNAKE_CASE				:Any)				-> Optional[Any]:






							'''simple docstring'''






							UpperCamelCase_    :  List[Any]				    =    JukeboxTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE)
							UpperCamelCase_    :  str				    =    tokenizer(**self.metas)["""input_ids"""]
							return tokens




		@slow
		def   _snake_case  (     self				:Union[str, Any])				-> List[str]:






							'''simple docstring'''






							UpperCamelCase_    :  Tuple				    =    JukeboxModel.from_pretrained(self.model_id ,						min_duration=0).eval()
							UpperCamelCase_    :  List[Any]				    =    self.prepare_inputs(self.model_id)

							set_seed(0)
							UpperCamelCase_    :  Optional[Any]				    =    [torch.zeros(1 ,						0 ,						dtype=torch.long).cpu() for _ in range(3)]
							UpperCamelCase_    :  List[Any]				    =    model._sample(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						[0] ,						sample_length=6_0 * model.priors[0].raw_to_tokens ,						save_results=_SCREAMING_SNAKE_CASE)
							self.assertIn(zs[0][0].detach().cpu().tolist() ,						[self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2])

							set_seed(0)
							UpperCamelCase_    :  Optional[int]				    =    model._sample(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						[1] ,						sample_length=6_0 * model.priors[1].raw_to_tokens ,						save_results=_SCREAMING_SNAKE_CASE)
							self.assertIn(zs[1][0].detach().cpu().tolist() ,						[self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2])

							set_seed(0)
							UpperCamelCase_    :  Tuple				    =    model._sample(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						[2] ,						sample_length=6_0 * model.priors[2].raw_to_tokens ,						save_results=_SCREAMING_SNAKE_CASE)
							self.assertIn(zs[2][0].detach().cpu().tolist() ,						[self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2])




		@slow
		@skip("""Not enough GPU memory on CI runners""")
		def   _snake_case  (     self				:Dict)				-> List[Any]:






							'''simple docstring'''






							UpperCamelCase_    :  Union[str, Any]				    =    JukeboxModel.from_pretrained(self.model_id ,						min_duration=0).eval()
							UpperCamelCase_    :  List[Any]				    =    [i.cuda() for i in self.prepare_inputs(self.model_id)]

							set_seed(0)
							model.priors[0].cuda()
							UpperCamelCase_    :  Any				    =    [torch.zeros(1 ,						0 ,						dtype=torch.long).cuda() for _ in range(3)]
							UpperCamelCase_    :  List[str]				    =    model._sample(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						[0] ,						sample_length=6_0 * model.priors[0].raw_to_tokens ,						save_results=_SCREAMING_SNAKE_CASE)
							torch.testing.assert_allclose(zs[0][0].cpu() ,						torch.tensor(self.EXPECTED_GPU_OUTPUTS_2))
							model.priors[0].cpu()

							set_seed(0)
							model.priors[1].cuda()
							UpperCamelCase_    :  Optional[Any]				    =    model._sample(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						[1] ,						sample_length=6_0 * model.priors[1].raw_to_tokens ,						save_results=_SCREAMING_SNAKE_CASE)
							torch.testing.assert_allclose(zs[1][0].cpu() ,						torch.tensor(self.EXPECTED_GPU_OUTPUTS_1))
							model.priors[1].cpu()

							set_seed(0)
							model.priors[2].cuda()
							UpperCamelCase_    :  int				    =    model._sample(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						[2] ,						sample_length=6_0 * model.priors[2].raw_to_tokens ,						save_results=_SCREAMING_SNAKE_CASE)
							torch.testing.assert_allclose(zs[2][0].cpu() ,						torch.tensor(self.EXPECTED_GPU_OUTPUTS_0))





		@slow
		def   _snake_case  (     self				:Tuple)				-> Tuple:






							'''simple docstring'''






							UpperCamelCase_    :  Dict				    =    """ArthurZ/jukebox_prior_0"""
							UpperCamelCase_    :  Any				    =    JukeboxPrior.from_pretrained(_SCREAMING_SNAKE_CASE ,						min_duration=0).eval().half().to("""cuda""")

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






'''simple docstring'''


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__SCREAMING_SNAKE_CASE  =			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE  =			{
    "gpt2": "https://huggingface.co/gpt2/resolve/main/config.json",
    "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/config.json",
    "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/config.json",
    "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/config.json",
    "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/config.json",
}




class 						__snake_case (						_a  ):
		a_        =				"gpt2"
		a_        =				["past_key_values"]
		a_        =				{
		    "hidden_size": "n_embd",
		    "max_position_embeddings": "n_positions",
		    "num_attention_heads": "n_head",
		    "num_hidden_layers": "n_layer",
		}




		def __init__(     self				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:Dict=5_0_2_5_7 ,						_SCREAMING_SNAKE_CASE				:str=1_0_2_4 ,						_SCREAMING_SNAKE_CASE				:Dict=7_6_8 ,						_SCREAMING_SNAKE_CASE				:List[Any]=1_2 ,						_SCREAMING_SNAKE_CASE				:Union[str, Any]=1_2 ,						_SCREAMING_SNAKE_CASE				:Any=None ,						_SCREAMING_SNAKE_CASE				:Any="gelu_new" ,						_SCREAMING_SNAKE_CASE				:Union[str, Any]=0.1 ,						_SCREAMING_SNAKE_CASE				:List[str]=0.1 ,						_SCREAMING_SNAKE_CASE				:Union[str, Any]=0.1 ,						_SCREAMING_SNAKE_CASE				:str=1E-5 ,						_SCREAMING_SNAKE_CASE				:str=0.02 ,						_SCREAMING_SNAKE_CASE				:Tuple="cls_index" ,						_SCREAMING_SNAKE_CASE				:Union[str, Any]=True ,						_SCREAMING_SNAKE_CASE				:Dict=None ,						_SCREAMING_SNAKE_CASE				:Tuple=True ,						_SCREAMING_SNAKE_CASE				:Dict=0.1 ,						_SCREAMING_SNAKE_CASE				:List[Any]=True ,						_SCREAMING_SNAKE_CASE				:Optional[Any]=True ,						_SCREAMING_SNAKE_CASE				:Tuple=5_0_2_5_6 ,						_SCREAMING_SNAKE_CASE				:Any=5_0_2_5_6 ,						_SCREAMING_SNAKE_CASE				:List[str]=False ,						_SCREAMING_SNAKE_CASE				:Any=False ,						**_SCREAMING_SNAKE_CASE				:List[Any] ,						)				-> Optional[Any]:






								'''simple docstring'''






								UpperCamelCase_    :  Optional[Any]				    =    vocab_size
								UpperCamelCase_    :  int				    =    n_positions
								UpperCamelCase_    :  Union[str, Any]				    =    n_embd
								UpperCamelCase_    :  Tuple				    =    n_layer
								UpperCamelCase_    :  Optional[Any]				    =    n_head
								UpperCamelCase_    :  List[Any]				    =    n_inner
								UpperCamelCase_    :  List[Any]				    =    activation_function
								UpperCamelCase_    :  Tuple				    =    resid_pdrop
								UpperCamelCase_    :  Optional[int]				    =    embd_pdrop
								UpperCamelCase_    :  Optional[Any]				    =    attn_pdrop
								UpperCamelCase_    :  List[str]				    =    layer_norm_epsilon
								UpperCamelCase_    :  Tuple				    =    initializer_range
								UpperCamelCase_    :  Union[str, Any]				    =    summary_type
								UpperCamelCase_    :  List[Any]				    =    summary_use_proj
								UpperCamelCase_    :  Tuple				    =    summary_activation
								UpperCamelCase_    :  Any				    =    summary_first_dropout
								UpperCamelCase_    :  Union[str, Any]				    =    summary_proj_to_labels
								UpperCamelCase_    :  Any				    =    scale_attn_weights
								UpperCamelCase_    :  List[str]				    =    use_cache
								UpperCamelCase_    :  int				    =    scale_attn_by_inverse_layer_idx
								UpperCamelCase_    :  int				    =    reorder_and_upcast_attn

								UpperCamelCase_    :  Union[str, Any]				    =    bos_token_id
								UpperCamelCase_    :  Optional[int]				    =    eos_token_id

								super().__init__(bos_token_id=_SCREAMING_SNAKE_CASE ,						eos_token_id=_SCREAMING_SNAKE_CASE ,						**_SCREAMING_SNAKE_CASE)







class 						__snake_case (						_a  ):




		def __init__(     self				:List[Any] ,						_SCREAMING_SNAKE_CASE				:PretrainedConfig ,						_SCREAMING_SNAKE_CASE				:str = "default" ,						_SCREAMING_SNAKE_CASE				:List[PatchingSpec] = None ,						_SCREAMING_SNAKE_CASE				:bool = False ,						)				-> Any:






								'''simple docstring'''






								super().__init__(_SCREAMING_SNAKE_CASE ,						task=_SCREAMING_SNAKE_CASE ,						patching_specs=_SCREAMING_SNAKE_CASE ,						use_past=_SCREAMING_SNAKE_CASE)
								if not getattr(self._config ,						"""pad_token_id""" ,						_SCREAMING_SNAKE_CASE):
														# TODO: how to do that better?
														UpperCamelCase_    :  List[str]				    =    0




		@property
		def   _snake_case  (     self				:Optional[int])				-> Mapping[str, Mapping[int, str]]:






								'''simple docstring'''






								UpperCamelCase_    :  str				    =    OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}})
								if self.use_past:
														self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE ,						direction="""inputs""")
														UpperCamelCase_    :  Any				    =    {0: """batch""", 1: """past_sequence + sequence"""}
								else:
														UpperCamelCase_    :  Optional[int]				    =    {0: """batch""", 1: """sequence"""}

								return common_inputs




		@property
		def   _snake_case  (     self				:Optional[int])				-> int:






								'''simple docstring'''






								return self._config.n_layer




		@property
		def   _snake_case  (     self				:Optional[int])				-> int:






								'''simple docstring'''






								return self._config.n_head




		def   _snake_case  (     self				:Optional[Any] ,						_SCREAMING_SNAKE_CASE				:PreTrainedTokenizer ,						_SCREAMING_SNAKE_CASE				:int = -1 ,						_SCREAMING_SNAKE_CASE				:int = -1 ,						_SCREAMING_SNAKE_CASE				:bool = False ,						_SCREAMING_SNAKE_CASE				:Optional[TensorType] = None ,						)				-> Mapping[str, Any]:






								'''simple docstring'''






								UpperCamelCase_    :  List[str]				    =    super(_SCREAMING_SNAKE_CASE ,						self).generate_dummy_inputs(
								    _SCREAMING_SNAKE_CASE ,						batch_size=_SCREAMING_SNAKE_CASE ,						seq_length=_SCREAMING_SNAKE_CASE ,						is_pair=_SCREAMING_SNAKE_CASE ,						framework=_SCREAMING_SNAKE_CASE)

								# We need to order the input in the way they appears in the forward()
								UpperCamelCase_    :  Optional[Any]				    =    OrderedDict({"""input_ids""": common_inputs["""input_ids"""]})

								# Need to add the past_keys
								if self.use_past:
														if not is_torch_available():
																				raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""")
														else:
																				import torch

																				UpperCamelCase_    ,UpperCamelCase_    :  str				    =    common_inputs["""input_ids"""].shape
																				# Not using the same length for past_key_values
																				UpperCamelCase_    :  Optional[int]				    =    seqlen + 2
																				UpperCamelCase_    :  Tuple				    =    (
																				    batch,
																				    self.num_attention_heads,
																				    past_key_values_length,
																				    self._config.hidden_size // self.num_attention_heads,
																				)
																				UpperCamelCase_    :  List[str]				    =    [
																				    (torch.zeros(_SCREAMING_SNAKE_CASE), torch.zeros(_SCREAMING_SNAKE_CASE)) for _ in range(self.num_layers)
																				]

								UpperCamelCase_    :  Optional[Any]				    =    common_inputs["""attention_mask"""]
								if self.use_past:
														UpperCamelCase_    :  Optional[Any]				    =    ordered_inputs["""attention_mask"""].dtype
														UpperCamelCase_    :  Tuple				    =    torch.cat(
														    [ordered_inputs["""attention_mask"""], torch.ones(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						dtype=_SCREAMING_SNAKE_CASE)] ,						dim=1)

								return ordered_inputs





		@property
		def   _snake_case  (     self				:int)				-> int:






								'''simple docstring'''






								return 1_3




