




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       ):
							_SCREAMING_SNAKE_CASE  :		Tuple			  =       (JukeboxModel,) if is_torch_available() else ()
							_SCREAMING_SNAKE_CASE  :		Optional[int]			  =       '''openai/jukebox-1b-lyrics'''
							_SCREAMING_SNAKE_CASE  :		List[Any]			  =       {
							    '''artist''': '''Zac Brown Band''',
							    '''genres''': '''Country''',
							    '''lyrics''': '''I met a traveller from an antique land,
    Who said "Two vast and trunkless legs of stone
    Stand in the desert. . . . Near them, on the sand,
    Half sunk a shattered visage lies, whose frown,
    And wrinkled lip, and sneer of cold command,
    Tell that its sculptor well those passions read
    Which yet survive, stamped on these lifeless things,
    The hand that mocked them, and the heart that fed;
    And on the pedestal, these words appear:
    My name is Ozymandias, King of Kings;
    Look on my Works, ye Mighty, and despair!
    Nothing beside remains. Round the decay
    Of that colossal Wreck, boundless and bare
    The lone and level sands stretch far away
    ''',
							}
							# fmt: off
							_SCREAMING_SNAKE_CASE  :		str			  =       [
							    18_64, 15_36, 12_13, 18_70, 13_57, 15_36, 5_19, 8_80, 13_23, 7_89, 10_82, 5_34,
							    10_00, 14_45, 11_05, 11_30, 9_67, 5_15, 14_34, 16_20, 5_34, 14_95, 2_83, 14_45,
							    3_33, 13_07, 5_39, 16_31, 15_28, 3_75, 14_34, 6_73, 6_27, 7_10, 7_78, 18_83,
							    14_05, 12_76, 14_55, 12_28
							]

							_SCREAMING_SNAKE_CASE  :		int			  =       [
							    14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
							    6_53, 6_53, 6_53, 6_53
							]

							_SCREAMING_SNAKE_CASE  :		List[str]			  =       [
							    11_25, 17_51, 6_97, 17_76, 11_41, 14_76, 3_91, 6_97, 11_25, 6_84, 8_67, 4_16,
							    8_44, 13_72, 12_74, 7_17, 12_74, 8_44, 12_99, 14_19, 6_97, 13_70, 3_17, 11_25,
							    1_91, 14_40, 13_70, 14_40, 13_70, 2_82, 16_21, 13_70, 3_68, 3_49, 8_67, 18_72,
							    12_62, 8_69, 17_28, 7_47
							]
							_SCREAMING_SNAKE_CASE  :		Optional[Any]			  =       [
							    4_16, 4_16, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
							    4_16, 4_16, 4_16, 4_16
							]

							_SCREAMING_SNAKE_CASE  :		Union[str, Any]			  =       [
							    17_55, 8_42, 3_07, 18_43, 10_22, 13_95, 2_34, 15_54, 8_06, 7_39, 10_22, 4_42,
							    6_16, 5_56, 2_68, 14_99, 9_33, 4_57, 14_40, 18_37, 7_55, 9_85, 3_08, 9_02,
							    2_93, 14_43, 16_71, 11_41, 15_33, 5_55, 15_62, 10_61, 2_87, 4_17, 10_22, 20_08,
							    11_86, 10_15, 17_77, 2_68
							]
							_SCREAMING_SNAKE_CASE  :		Tuple			  =       [
							    8_54, 8_42, 13_53, 1_14, 13_53, 8_42, 1_85, 8_42, 1_85, 1_14, 5_91, 8_42,
							    1_85, 4_17, 1_85, 8_42, 3_07, 8_42, 5_91, 8_42, 1_85, 8_42, 3_07, 8_42,
							    5_91, 8_42, 13_53, 8_42, 1_85, 8_42, 5_91, 8_42, 5_91, 1_14, 5_91, 8_42,
							    1_85, 8_42, 5_91, 89
							]

							_SCREAMING_SNAKE_CASE  :		Dict			  =       [1_05_83_04, 0, 78_64_32, 71_69, 5_07, 76, 27, 40, 30, 76]

							_SCREAMING_SNAKE_CASE  :		List[Any]			  =       [
							    3_90, 11_60, 10_02, 19_07, 17_88, 17_88, 17_88, 19_07, 10_02, 10_02, 18_54, 10_02,
							    10_02, 10_02, 10_02, 10_02, 10_02, 11_60, 11_60, 16_06, 5_96, 5_96, 11_60, 10_02,
							    15_16, 5_96, 10_02, 10_02, 10_02, 19_07, 17_88, 17_88, 17_88, 18_54, 17_88, 19_07,
							    19_07, 17_88, 5_96, 16_26
							]
							_SCREAMING_SNAKE_CASE  :		int			  =       [
							    12_36, 16_68, 14_84, 19_20, 18_48, 14_09, 1_39, 8_64, 18_28, 12_72, 15_99, 8_24,
							    16_72, 1_39, 5_55, 14_84, 8_24, 19_20, 5_55, 5_96, 15_79, 15_99, 12_31, 15_99,
							    16_37, 14_07, 2_12, 8_24, 15_99, 1_16, 14_33, 8_24, 2_58, 15_99, 14_33, 18_95,
							    10_63, 14_33, 14_33, 15_99
							]
							_SCREAMING_SNAKE_CASE  :		List[Any]			  =       [
							    16_84, 18_73, 11_19, 11_89, 3_95, 6_11, 19_01, 9_72, 8_90, 13_37, 13_92, 19_27,
							    96, 9_72, 6_72, 7_80, 11_19, 8_90, 1_58, 7_71, 10_73, 19_27, 3_53, 13_31,
							    12_69, 14_59, 13_33, 16_45, 8_12, 15_77, 13_37, 6_06, 3_53, 9_81, 14_66, 6_19,
							    1_97, 3_91, 3_02, 19_30
							]
							_SCREAMING_SNAKE_CASE  :		Union[str, Any]			  =       [
							    3_90, 11_60, 10_02, 19_07, 17_88, 17_88, 17_88, 19_07, 10_02, 10_02, 18_54, 10_02,
							    10_02, 10_02, 10_02, 10_02, 10_02, 11_60, 11_60, 16_06, 5_96, 5_96, 11_60, 10_02,
							    15_16, 5_96, 10_02, 10_02, 10_02, 19_07, 17_88, 17_88, 17_88, 18_54, 17_88, 19_07,
							    19_07, 17_88, 5_96, 16_26
							]
							_SCREAMING_SNAKE_CASE  :		Union[str, Any]			  =       [
							    -0.0492, -0.0524, -0.0565, -0.0640, -0.0686, -0.0684, -0.0677, -0.0664,
							    -0.0605, -0.0490, -0.0330, -0.0168, -0.0083, -0.0075, -0.0051, 0.0025,
							    0.0136, 0.0261, 0.0386, 0.0497, 0.0580, 0.0599, 0.0583, 0.0614,
							    0.0740, 0.0889, 0.1023, 0.1162, 0.1211, 0.1212, 0.1251, 0.1336,
							    0.1502, 0.1686, 0.1883, 0.2148, 0.2363, 0.2458, 0.2507, 0.2531
							]
							_SCREAMING_SNAKE_CASE  :		Dict			  =       [
							    0.0256, -0.0544, 0.1600, -0.0032, 0.1066, 0.0825, -0.0013, 0.3440,
							    0.0210, 0.0412, -0.1777, -0.0892, -0.0164, 0.0285, -0.0613, -0.0617,
							    -0.0137, -0.0201, -0.0175, 0.0215, -0.0627, 0.0520, -0.0730, 0.0970,
							    -0.0100, 0.0442, -0.0586, 0.0207, -0.0015, -0.0082
							]
							_SCREAMING_SNAKE_CASE  :		str			  =       [
							    0.0415, 0.0877, 0.0022, -0.0055, 0.0751, 0.0334, 0.0324, -0.0068,
							    0.0011, 0.0017, -0.0676, 0.0655, -0.0143, 0.0399, 0.0303, 0.0743,
							    -0.0168, -0.0394, -0.1113, 0.0124, 0.0442, 0.0267, -0.0003, -0.1536,
							    -0.0116, -0.1837, -0.0180, -0.1026, -0.0777, -0.0456
							]
							_SCREAMING_SNAKE_CASE  :		Union[str, Any]			  =       [
							    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   :	Tuple				)		->					Any:

										"""simple docstring"""



										UpperCamelCase_     				= JukeboxTokenizer.from_pretrained(self.model_id				)
										UpperCamelCase_     				= tokenizer(**self.metas				)['input_ids']
										return tokens







							@slow
							def        lowercase__							(    self   :	Union[str, Any]				)		->					int:

										"""simple docstring"""



										UpperCamelCase_     				= JukeboxModel.from_pretrained(self.model_id   ,     min_duration=0				).eval()
										UpperCamelCase_     				= self.prepare_inputs()

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

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

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







							@slow
							def        lowercase__							(    self   :	Union[str, Any]				)		->					Optional[Any]:

										"""simple docstring"""



										UpperCamelCase_     				= False
										UpperCamelCase_     				= JukeboxModel.from_pretrained(self.model_id   ,     min_duration=0				).eval()

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

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

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

										UpperCamelCase_				,   UpperCamelCase_				,   UpperCamelCase_     				= top_prior.get_cond(__UpperCAmelCase   ,     __UpperCAmelCase				)
										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]				)		->					Union[str, Any]:

										"""simple docstring"""



										UpperCamelCase_     				= False

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

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

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

										UpperCamelCase_     				= torch.cat((zs[1], torch.zeros(1   ,     2048 - zs[1].shape[-1]				))   ,     dim=-1				).long()
										UpperCamelCase_     				= [upper_a, upper_a, model.vqvae.encode(__UpperCAmelCase   ,     start_level=0   ,     bs_chunks=waveform.shape[0]				)[0]]
										UpperCamelCase_     				= model._sample(
										    __UpperCAmelCase   ,     __UpperCAmelCase   ,     sample_levels=[2]   ,     save_results=__UpperCAmelCase   ,     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   :	str				)		->					Optional[Any]:

										"""simple docstring"""



										UpperCamelCase_     				= JukeboxModel.from_pretrained(self.model_id   ,     min_duration=0				).eval()
										set_seed(0				)
										UpperCamelCase_     				= torch.rand((1, 5120, 1)				)
										with torch.no_grad():
													UpperCamelCase_     				= model.vqvae.encode(__UpperCAmelCase   ,     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_     				= model.vqvae.decode(__UpperCAmelCase   ,     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       ):
							_SCREAMING_SNAKE_CASE  :		int			  =       (JukeboxModel,) if is_torch_available() else ()
							_SCREAMING_SNAKE_CASE  :		List[Any]			  =       '''openai/jukebox-5b-lyrics'''
							_SCREAMING_SNAKE_CASE  :		str			  =       {
							    '''artist''': '''Zac Brown Band''',
							    '''genres''': '''Country''',
							    '''lyrics''': '''I met a traveller from an antique land,
    Who said "Two vast and trunkless legs of stone
    Stand in the desert. . . . Near them, on the sand,
    Half sunk a shattered visage lies, whose frown,
    And wrinkled lip, and sneer of cold command,
    Tell that its sculptor well those passions read
    Which yet survive, stamped on these lifeless things,
    The hand that mocked them, and the heart that fed;
    And on the pedestal, these words appear:
    My name is Ozymandias, King of Kings;
    Look on my Works, ye Mighty, and despair!
    Nothing beside remains. Round the decay
    Of that colossal Wreck, boundless and bare
    The lone and level sands stretch far away
    ''',
							}

							# fmt: off
							_SCREAMING_SNAKE_CASE  :		Optional[int]			  =       [
							    14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
							    14_89, 14_89, 14_89, 14_89, 11_50, 18_53, 15_09, 11_50, 13_57, 15_09, 6, 12_72
							]
							_SCREAMING_SNAKE_CASE  :		str			  =       [
							    14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53
							]

							_SCREAMING_SNAKE_CASE  :		Tuple			  =       [
							    11_25, 4_16, 11_25, 11_25, 11_25, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16,
							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
							]
							_SCREAMING_SNAKE_CASE  :		Union[str, Any]			  =       [
							    4_16, 4_16, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
							]

							_SCREAMING_SNAKE_CASE  :		str			  =       [
							    17_55, 10_61, 2_34, 17_55, 10_61, 17_55, 1_85, 2_90, 3_07, 3_07, 6_16, 6_16,
							    6_16, 6_16, 6_16, 6_16, 3_07, 2_90, 4_17, 17_55, 2_34, 17_55, 1_85, 2_90,
							    2_90, 2_90, 3_07, 6_16, 6_16, 6_16, 6_16, 6_16, 2_90, 2_34, 2_34, 17_55,
							    2_34, 2_34, 17_55, 2_34, 1_85, 1_85, 3_07, 6_16, 6_16, 6_16, 6_16, 2_90,
							    17_55, 17_55, 17_55, 2_34, 2_34, 17_55, 15_72, 2_90, 3_07, 6_16, 34, 6_16
							]
							_SCREAMING_SNAKE_CASE  :		Any			  =       [
							    8_54, 8_42, 13_53, 1_14, 13_53, 8_42, 1_85, 8_42, 1_85, 1_14, 5_91, 8_42, 1_85,
							    4_17, 1_85, 8_42, 3_07, 8_42, 5_91, 8_42, 1_85, 8_42, 1_85, 8_42, 5_91, 8_42,
							    13_53, 8_42, 1_85, 8_42, 5_91, 8_42, 5_91, 1_14, 5_91, 8_42, 1_85, 8_42, 5_91,
							    89, 5_91, 8_42, 5_91, 8_42, 5_91, 4_17, 13_72, 8_42, 13_72, 8_42, 34, 8_42,
							    1_85, 89, 5_91, 8_42, 1_85, 8_42, 5_91, 6_32
							]

							_SCREAMING_SNAKE_CASE  :		Any			  =       [
							    14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53
							]
							_SCREAMING_SNAKE_CASE  :		Tuple			  =       [
							    14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 18_53, 11_77, 15_36, 12_28,
							    7_10, 4_75, 14_89, 12_29, 12_24, 2_31, 12_24, 2_52, 14_34, 6_53, 4_75,
							    11_06, 18_77, 15_99, 12_28, 16_00, 16_83, 11_82, 18_53, 4_75, 18_64,
							    2_52, 12_29, 14_34, 20_01
							]

							_SCREAMING_SNAKE_CASE  :		Tuple			  =       [
							    11_25, 11_25, 4_16, 11_25, 11_25, 4_16, 11_25, 11_25, 4_16, 4_16, 11_25, 4_16,
							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
							]
							_SCREAMING_SNAKE_CASE  :		Dict			  =       [
							    4_91, 17_55, 34, 16_13, 17_55, 4_17, 9_92, 16_13, 2_22, 8_42, 13_53, 16_13,
							    8_44, 6_32, 1_85, 16_13, 8_44, 6_32, 1_85, 16_13, 1_85, 8_42, 6_77, 16_13,
							    1_85, 1_14, 13_53, 16_13, 3_07, 89, 8_44, 16_13, 3_07, 13_32, 2_34, 19_79,
							    3_07, 89, 13_53, 6_16, 34, 8_42, 1_85, 8_42, 34, 8_42, 1_85, 8_42,
							    3_07, 1_14, 1_85, 89, 34, 12_68, 1_85, 89, 34, 8_42, 1_85, 89
							]







							def        lowercase__							(    self   :	int   ,     __UpperCAmelCase   :	Union[str, Any]				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= JukeboxTokenizer.from_pretrained(__UpperCAmelCase				)
										UpperCamelCase_     				= tokenizer(**self.metas				)['input_ids']
										return tokens







							@slow
							def        lowercase__							(    self   :	Dict				)		->					Any:

										"""simple docstring"""



										UpperCamelCase_     				= JukeboxModel.from_pretrained(self.model_id   ,     min_duration=0				).eval()
										UpperCamelCase_     				= self.prepare_inputs(self.model_id				)

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

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

										set_seed(0				)
										UpperCamelCase_     				= model._sample(__UpperCAmelCase   ,     __UpperCAmelCase   ,     [2]   ,     sample_length=60 * model.priors[2].raw_to_tokens   ,     save_results=__UpperCAmelCase				)
										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   :	Any				)		->					Union[str, Any]:

										"""simple docstring"""



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

										set_seed(0				)
										model.priors[0].cuda()
										UpperCamelCase_     				= [torch.zeros(1   ,     0   ,     dtype=torch.long				).cuda() for _ in range(3				)]
										UpperCamelCase_     				= model._sample(__UpperCAmelCase   ,     __UpperCAmelCase   ,     [0]   ,     sample_length=60 * model.priors[0].raw_to_tokens   ,     save_results=__UpperCAmelCase				)
										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_     				= model._sample(__UpperCAmelCase   ,     __UpperCAmelCase   ,     [1]   ,     sample_length=60 * model.priors[1].raw_to_tokens   ,     save_results=__UpperCAmelCase				)
										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_     				= model._sample(__UpperCAmelCase   ,     __UpperCAmelCase   ,     [2]   ,     sample_length=60 * model.priors[2].raw_to_tokens   ,     save_results=__UpperCAmelCase				)
										torch.testing.assert_allclose(zs[2][0].cpu()   ,     torch.tensor(self.EXPECTED_GPU_OUTPUTS_0				)				)







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

										"""simple docstring"""



										UpperCamelCase_     				= 'ArthurZ/jukebox_prior_0'
										UpperCamelCase_     				= JukeboxPrior.from_pretrained(__UpperCAmelCase   ,     min_duration=0				).eval().half().to('cuda'				)

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






import numpy as np




def  a_		(							__snake_case     ,		__snake_case					)	->			List[str]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= abs(predict - actual					)
			UpperCamelCase_     				= difference.mean()

			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.square(__snake_case					)

			UpperCamelCase_     				= square_diff.mean()
			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.square(__snake_case					)
			UpperCamelCase_     				= square_diff.mean()
			UpperCamelCase_     				= np.sqrt(__snake_case					)
			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[int]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= np.log(predict + 1					)
			UpperCamelCase_     				= np.log(actual + 1					)

			UpperCamelCase_     				= log_predict - log_actual
			UpperCamelCase_     				= np.square(__snake_case					)
			UpperCamelCase_     				= square_diff.mean()

			UpperCamelCase_     				= np.sqrt(__snake_case					)

			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			int:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.sum(__snake_case					) / len(__snake_case					)
			UpperCamelCase_     				= np.sum(__snake_case					) / len(__snake_case					)
			# print(numerator, denumerator)
			UpperCamelCase_     				= float(__snake_case					) / denumerator * 1_0_0

			return score





def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			return np.mean(np.array(__snake_case					) == np.array(__snake_case					)					)
