
'''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"""




   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		= [
       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
   ]

   A		= [
       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
   ]

   A		= [
       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
   ]
   A		= [
       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
   ]

   A		= [
       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
   ]
   A		= [
       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
   ]

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

   A		= [
       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
   ]
   A		= [
       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
   ]
   A		= [
       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
   ]
   A		= [
       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
   ]
   A		= [
       -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
   ]
   A		= [
       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
   ]
   A		= [
       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
   ]
   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 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		JukeboxTokenizer.from_pretrained(self.model_id      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tokenizer(**self.metas      )['input_ids']
    return tokens






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		JukeboxModel.from_pretrained(self.model_id    ,min_duration=0      ).eval()
    SCREAMING_SNAKE_CASE  :  str								=		self.prepare_inputs()

    set_seed(0      )
    SCREAMING_SNAKE_CASE  :  Any								=		[torch.zeros(1    ,0    ,dtype=torch.long      ).cpu() for _ in range(3      )]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		model._sample(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,[0]    ,sample_length=40 * 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      )
    SCREAMING_SNAKE_CASE  :  Dict								=		model._sample(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,[1]    ,sample_length=40 * 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      )
    SCREAMING_SNAKE_CASE  :  Any								=		model._sample(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,[2]    ,sample_length=40 * 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 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		False
    SCREAMING_SNAKE_CASE  :  Tuple								=		JukeboxModel.from_pretrained(self.model_id    ,min_duration=0      ).eval()

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

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

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

    SCREAMING_SNAKE_CASE  :  Any								=		top_prior.get_cond(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
    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 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		False

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		JukeboxModel.from_pretrained(self.model_id    ,min_duration=0      ).eval()
    set_seed(0      )
    SCREAMING_SNAKE_CASE  :  int								=		torch.rand((1, 5120, 1)      )
    SCREAMING_SNAKE_CASE  :  Tuple								=		list(self.prepare_inputs()      )

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

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

    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		torch.cat((zs[1], torch.zeros(1    ,2048 - zs[1].shape[-1]      ))    ,dim=-1      ).long()
    SCREAMING_SNAKE_CASE  :  List[Any]								=		[upper_a, upper_a, model.vqvae.encode(__SCREAMING_SNAKE_CASE    ,start_level=0    ,bs_chunks=waveform.shape[0]      )[0]]
    SCREAMING_SNAKE_CASE  :  List[str]								=		model._sample(
        __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,sample_levels=[2]    ,save_results=__SCREAMING_SNAKE_CASE    ,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 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  str								=		JukeboxModel.from_pretrained(self.model_id    ,min_duration=0      ).eval()
    set_seed(0      )
    SCREAMING_SNAKE_CASE  :  Dict								=		torch.rand((1, 5120, 1)      )
    with torch.no_grad():
     SCREAMING_SNAKE_CASE  :  str								=		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():
     SCREAMING_SNAKE_CASE  :  Tuple								=		model.vqvae.decode(__SCREAMING_SNAKE_CASE    ,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"""




   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		= [
       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
   ]
   A		= [
       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
   ]

   A		= [
       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
   ]
   A		= [
       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
   ]

   A		= [
       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
   ]
   A		= [
       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
   ]

   A		= [
       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
   ]
   A		= [
       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
   ]

   A		= [
       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
   ]
   A		= [
       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 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		JukeboxTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		tokenizer(**self.metas      )['input_ids']
    return tokens






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		JukeboxModel.from_pretrained(self.model_id    ,min_duration=0      ).eval()
    SCREAMING_SNAKE_CASE  :  Dict								=		self.prepare_inputs(self.model_id      )

    set_seed(0      )
    SCREAMING_SNAKE_CASE  :  Dict								=		[torch.zeros(1    ,0    ,dtype=torch.long      ).cpu() for _ in range(3      )]
    SCREAMING_SNAKE_CASE  :  int								=		model._sample(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,[0]    ,sample_length=60 * 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      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		model._sample(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,[1]    ,sample_length=60 * 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      )
    SCREAMING_SNAKE_CASE  :  Dict								=		model._sample(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,[2]    ,sample_length=60 * 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 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		JukeboxModel.from_pretrained(self.model_id    ,min_duration=0      ).eval()
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		[i.cuda() for i in self.prepare_inputs(self.model_id      )]

    set_seed(0      )
    model.priors[0].cuda()
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		[torch.zeros(1    ,0    ,dtype=torch.long      ).cuda() for _ in range(3      )]
    SCREAMING_SNAKE_CASE  :  List[Any]								=		model._sample(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,[0]    ,sample_length=60 * 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()
    SCREAMING_SNAKE_CASE  :  List[str]								=		model._sample(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,[1]    ,sample_length=60 * 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()
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		model._sample(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,[2]    ,sample_length=60 * 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 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		'ArthurZ/jukebox_prior_0'
    SCREAMING_SNAKE_CASE  :  Dict								=		JukeboxPrior.from_pretrained(__SCREAMING_SNAKE_CASE    ,min_duration=0      ).eval().half().to('cuda'      )

    SCREAMING_SNAKE_CASE  :  str								=		self.prepare_inputs(__SCREAMING_SNAKE_CASE      )[0].cuda()
    SCREAMING_SNAKE_CASE  :  List[str]								=		model.get_metadata(__SCREAMING_SNAKE_CASE    ,0    ,7680    ,0      )
    set_seed(0      )
    SCREAMING_SNAKE_CASE  :  Tuple								=		model.sample(1    ,metadata=__SCREAMING_SNAKE_CASE    ,sample_tokens=60      )
    self.assertIn(outputs[0].cpu().tolist()    ,[self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]      )









'''simple docstring'''


from __future__ import annotations




def       A							(      _UpperCAmelCase   :				list					)  ->				int | float:


  '''simple docstring'''




  __lowerCAmelCase		:    List[Any]			     =  sorted(_UpperCAmelCase					)
  __lowerCAmelCase		:    List[str]			     =  len(_UpperCAmelCase					)
  __lowerCAmelCase		:    Any			     =  length >> 1
  return (
      (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
      if length % 2 == 0
      else sorted_list[mid_index]
  )




def       A							(      )  ->				Union[str, Any]:


  '''simple docstring'''




  import doctest

  doctest.testmod()


if __name__ == "__main__":
    main()





