


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    __lowercase  (       unittest.TestCase							):

       """simple docstring"""


       SCREAMING_SNAKE_CASE   :					Tuple        =						(JukeboxModel,) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE   :					Union[str, Any]        =						"openai/jukebox-1b-lyrics"
       SCREAMING_SNAKE_CASE   :					List[str]        =						{
           "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
       SCREAMING_SNAKE_CASE   :					Optional[Any]        =						[
           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   :					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
       ]

       SCREAMING_SNAKE_CASE   :					Optional[Any]        =						[
           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   :					List[str]        =						[
           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   :					List[str]        =						[
           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   :					List[str]        =						[
           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   :					Tuple        =						[1_05_83_04, 0, 78_64_32, 71_69, 5_07, 76, 27, 40, 30, 76]

       SCREAMING_SNAKE_CASE   :					str        =						[
           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   :					Dict        =						[
           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   :					Dict        =						[
           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   :					Dict        =						[
           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   :					List[str]        =						[
           -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   :					int        =						[
           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   :					Union[str, Any]        =						[
           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   :					Tuple        =						[
           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 		__magic_name__			(    self	)->	List[str]:
           _SCREAMING_SNAKE_CASE      = JukeboxTokenizer.from_pretrained(self.model_id	)
           _SCREAMING_SNAKE_CASE      = tokenizer(**self.metas	)['input_ids']
           return tokens





       @slow
       def 		__magic_name__			(    self	)->	List[str]:
           _SCREAMING_SNAKE_CASE      = JukeboxModel.from_pretrained(self.model_id				, min_duration=0	).eval()
           _SCREAMING_SNAKE_CASE      = self.prepare_inputs()

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

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

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





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

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

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

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

           _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = top_prior.get_cond(A_				, A_	)
           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 		__magic_name__			(    self	)->	List[Any]:
           _SCREAMING_SNAKE_CASE      = False

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

           _SCREAMING_SNAKE_CASE      = [model.vqvae.encode(A_				, start_level=2				, bs_chunks=waveform.shape[0]	)[0], None, None]
           _SCREAMING_SNAKE_CASE      = model._sample(
               A_				, A_				, sample_levels=[0]				, save_results=A_				, 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      = torch.cat((zs[0], torch.zeros(1				, 2048 - zs[0].shape[-1]	))				, dim=-1	).long()
           _SCREAMING_SNAKE_CASE      = [upper_a, model.vqvae.encode(A_				, start_level=1				, bs_chunks=waveform.shape[0]	)[0], None]
           _SCREAMING_SNAKE_CASE      = model._sample(
               A_				, A_				, sample_levels=[1]				, save_results=A_				, 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      = torch.cat((zs[1], torch.zeros(1				, 2048 - zs[1].shape[-1]	))				, dim=-1	).long()
           _SCREAMING_SNAKE_CASE      = [upper_a, upper_a, model.vqvae.encode(A_				, start_level=0				, bs_chunks=waveform.shape[0]	)[0]]
           _SCREAMING_SNAKE_CASE      = model._sample(
               A_				, A_				, sample_levels=[2]				, save_results=A_				, 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 		__magic_name__			(    self	)->	int:
           _SCREAMING_SNAKE_CASE      = JukeboxModel.from_pretrained(self.model_id				, min_duration=0	).eval()
           set_seed(0	)
           _SCREAMING_SNAKE_CASE      = torch.rand((1, 5120, 1)	)
           with torch.no_grad():
               _SCREAMING_SNAKE_CASE      = model.vqvae.encode(A_				, 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      = model.vqvae.decode(A_				, 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    __lowercase  (       unittest.TestCase							):

       """simple docstring"""


       SCREAMING_SNAKE_CASE   :					List[Any]        =						(JukeboxModel,) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE   :					Union[str, Any]        =						"openai/jukebox-5b-lyrics"
       SCREAMING_SNAKE_CASE   :					List[Any]        =						{
           "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
       SCREAMING_SNAKE_CASE   :					Dict        =						[
           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   :					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, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           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, 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   :					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, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 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   :					Dict        =						[
           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   :					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, 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   :					Optional[int]        =						[
           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   :					Tuple        =						[
           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 		__magic_name__			(    self				, A_	)->	str:
           _SCREAMING_SNAKE_CASE      = JukeboxTokenizer.from_pretrained(A_	)
           _SCREAMING_SNAKE_CASE      = tokenizer(**self.metas	)['input_ids']
           return tokens





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

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

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

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

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





       @slow
       def 		__magic_name__			(    self	)->	Union[str, Any]:
           _SCREAMING_SNAKE_CASE      = 'ArthurZ/jukebox_prior_0'
           _SCREAMING_SNAKE_CASE      = JukeboxPrior.from_pretrained(A_				, min_duration=0	).eval().half().to('cuda'	)

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




import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
							import torch_xla.core.xla_model as xm


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__							):

				"""simple docstring"""






				if isinstance(UpperCAmelCase__   ,(list, tuple)							):
								return honor_type(UpperCAmelCase__   ,(move_to_device(UpperCAmelCase__   ,UpperCAmelCase__							) for t in state)							)
				elif isinstance(UpperCAmelCase__   ,UpperCAmelCase__							):
								return type(UpperCAmelCase__							)({k: move_to_device(UpperCAmelCase__   ,UpperCAmelCase__							) for k, v in state.items()}							)
				elif isinstance(UpperCAmelCase__   ,torch.Tensor							):
								return state.to(UpperCAmelCase__							)
				return state



class    __lowercase  (       torch.optim.Optimizer							):

							"""simple docstring"""


							def __init__(    self				, A_				, A_=True				, A_=None	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = optimizer
											_SCREAMING_SNAKE_CASE      = scaler
											_SCREAMING_SNAKE_CASE      = AcceleratorState()
											_SCREAMING_SNAKE_CASE      = GradientState()
											_SCREAMING_SNAKE_CASE      = device_placement
											_SCREAMING_SNAKE_CASE      = False
											_SCREAMING_SNAKE_CASE      = None

											# Handle device placement
											if device_placement:
															_SCREAMING_SNAKE_CASE      = self.optimizer.state_dict()
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																			xm.send_cpu_data_to_device(A_				, self.accelerator_state.device	)
															else:
																			_SCREAMING_SNAKE_CASE      = move_to_device(A_				, self.accelerator_state.device	)
															self.optimizer.load_state_dict(A_	)





							@property
							def 		__magic_name__			(    self	)->	Tuple:
											return self.optimizer.state





							@state.setter
							def 		__magic_name__			(    self				, A_	)->	str:
											_SCREAMING_SNAKE_CASE      = state





							@property
							def 		__magic_name__			(    self	)->	Optional[Any]:
											return self.optimizer.param_groups





							@param_groups.setter
							def 		__magic_name__			(    self				, A_	)->	Tuple:
											_SCREAMING_SNAKE_CASE      = param_groups





							@property
							def 		__magic_name__			(    self	)->	Any:
											return self.optimizer.defaults





							@defaults.setter
							def 		__magic_name__			(    self				, A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = defaults





							def 		__magic_name__			(    self				, A_	)->	Any:
											self.optimizer.add_param_group(A_	)





							def 		__magic_name__			(    self				, A_	)->	int:
											if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
															xm.send_cpu_data_to_device(A_				, self.accelerator_state.device	)
											self.optimizer.load_state_dict(A_	)





							def 		__magic_name__			(    self	)->	List[str]:
											return self.optimizer.state_dict()





							def 		__magic_name__			(    self				, A_=None	)->	Any:
											if self.gradient_state.sync_gradients:
															_SCREAMING_SNAKE_CASE      = 'set_to_none' in inspect.signature(self.optimizer.zero_grad	).parameters
															if accept_arg:
																			if set_to_none is None:
																							_SCREAMING_SNAKE_CASE      = False
																			self.optimizer.zero_grad(set_to_none=A_	)
															else:
																			if set_to_none is not None:
																							raise ValueError('`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'	)
																			self.optimizer.zero_grad()





							def 		__magic_name__			(    self				, A_=None	)->	Optional[int]:
											if self.gradient_state.sync_gradients:
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																			_SCREAMING_SNAKE_CASE      = {'closure': closure} if closure is not None else {}
																			xm.optimizer_step(self.optimizer				, optimizer_args=A_	)
															elif self.scaler is not None:
																			_SCREAMING_SNAKE_CASE      = False
																			if self._last_scale is None:
																							# `get_scale` is an async operation requiring full synchronization
																							# on CPU and GPUs before finishing. As a result, we store away
																							# the prior one to reduce the call overhead
																							_SCREAMING_SNAKE_CASE      = self.scaler.get_scale()
																							_SCREAMING_SNAKE_CASE      = True
																			self.scaler.step(self.optimizer				, A_	)
																			self.scaler.update()
																			_SCREAMING_SNAKE_CASE      = self.scaler.get_scale()
																			if not new_scale:
																							# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																							_SCREAMING_SNAKE_CASE      = scale_after < self._last_scale
																			_SCREAMING_SNAKE_CASE      = scale_after

															else:
																			self.optimizer.step(A_	)





							def 		__magic_name__			(    self				, A_	)->	Dict:
											for param_group in self.optimizer.param_groups:
															_SCREAMING_SNAKE_CASE      = [parameters_map.get(A_				, A_	) for p in param_group['params']]





							@property
							def 		__magic_name__			(    self	)->	str:
											warnings.warn(
											    'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
											    '`optimizer.step_was_skipped` instead.'				, A_				, )
											return self._is_overflow





							@property
							def 		__magic_name__			(    self	)->	int:
											return self._is_overflow





							def __getstate__(    self	)->	Dict:
											return self.__dict__.copy()





							def __setstate__(    self				, A_	)->	List[str]:
											self.__dict__.update(A_	)
