



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 				lowerCAmelCase__	(						unittest.TestCase    ):
					__A  :     Union[str, Any]     =    (JukeboxModel,) if is_torch_available() else ()
					__A  :     str     =    'openai/jukebox-1b-lyrics'
					__A  :     Union[str, 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
					__A  :     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
					]

					__A  :     List[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
					]

					__A  :     int     =    [
					    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  :     Optional[int]     =    [
					    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  :     Optional[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
					]
					__A  :     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
					]

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

					__A  :     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
					]
					__A  :     Optional[Any]     =    [
					    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  :     List[str]     =    [
					    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  :     Optional[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
					]
					__A  :     Any     =    [
					    -0.0_492, -0.0_524, -0.0_565, -0.0_640, -0.0_686, -0.0_684, -0.0_677, -0.0_664,
					    -0.0_605, -0.0_490, -0.0_330, -0.0_168, -0.0_083, -0.0_075, -0.0_051, 0.0_025,
					    0.0_136, 0.0_261, 0.0_386, 0.0_497, 0.0_580, 0.0_599, 0.0_583, 0.0_614,
					    0.0_740, 0.0_889, 0.1_023, 0.1_162, 0.1_211, 0.1_212, 0.1_251, 0.1_336,
					    0.1_502, 0.1_686, 0.1_883, 0.2_148, 0.2_363, 0.2_458, 0.2_507, 0.2_531
					]
					__A  :     Any     =    [
					    0.0_256, -0.0_544, 0.1_600, -0.0_032, 0.1_066, 0.0_825, -0.0_013, 0.3_440,
					    0.0_210, 0.0_412, -0.1_777, -0.0_892, -0.0_164, 0.0_285, -0.0_613, -0.0_617,
					    -0.0_137, -0.0_201, -0.0_175, 0.0_215, -0.0_627, 0.0_520, -0.0_730, 0.0_970,
					    -0.0_100, 0.0_442, -0.0_586, 0.0_207, -0.0_015, -0.0_082
					]
					__A  :     Union[str, Any]     =    [
					    0.0_415, 0.0_877, 0.0_022, -0.0_055, 0.0_751, 0.0_334, 0.0_324, -0.0_068,
					    0.0_011, 0.0_017, -0.0_676, 0.0_655, -0.0_143, 0.0_399, 0.0_303, 0.0_743,
					    -0.0_168, -0.0_394, -0.1_113, 0.0_124, 0.0_442, 0.0_267, -0.0_003, -0.1_536,
					    -0.0_116, -0.1_837, -0.0_180, -0.1_026, -0.0_777, -0.0_456
					]
					__A  :     List[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   :  int):
											A__						:				Tuple         =			JukeboxTokenizer.from_pretrained(self.model_id)
											A__						:				List[str]         =			tokenizer(**self.metas)["input_ids"]
											return tokens







					@slow
					def 			_lowercase      (							self   :  List[Any]):
											A__						:				List[str]         =			JukeboxModel.from_pretrained(self.model_id      ,		min_duration=0).eval()
											A__						:				Optional[Any]         =			self.prepare_inputs()

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

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

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







					@slow
					def 			_lowercase      (							self   :  str):
											A__						:				Union[str, Any]         =			False
											A__						:				List[Any]         =			JukeboxModel.from_pretrained(self.model_id      ,		min_duration=0).eval()

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

											A__						:				Optional[int]         =			model.priors[0]
											A__						:				int         =			0
											A__						:				Union[str, Any]         =			top_prior.get_music_tokens_conds(lowercase__      ,		start=lowercase__      ,		end=start + top_prior.n_ctx)
											A__						:				List[str]         =			top_prior.get_metadata(labels[0].clone()      ,		lowercase__      ,		105_8304      ,		0)

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

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







					@slow
					def 			_lowercase      (							self   :  Any):
											A__						:				Any         =			False

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

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

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

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







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

											with torch.no_grad():
																	A__						:				str         =			model.vqvae.decode(lowercase__      ,		start_level=2      ,		bs_chunks=x.shape[0])
											torch.testing.assert_allclose(x[0, :40, 0]      ,		torch.tensor(self.EXPECTED_VQVAE_DECODE)      ,		atol=1e-4      ,		rtol=1e-4)




@require_torch
class 				lowerCAmelCase__	(						unittest.TestCase    ):
					__A  :     List[Any]     =    (JukeboxModel,) if is_torch_available() else ()
					__A  :     List[Any]     =    'openai/jukebox-5b-lyrics'
					__A  :     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
					__A  :     List[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,
					    14_89, 14_89, 14_89, 14_89, 11_50, 18_53, 15_09, 11_50, 13_57, 15_09, 6, 12_72
					]
					__A  :     List[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
					]

					__A  :     Dict     =    [
					    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  :     int     =    [
					    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  :     List[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
					]
					__A  :     int     =    [
					    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  :     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,
					    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  :     Optional[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
					]

					__A  :     str     =    [
					    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  :     Any     =    [
					    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   :  Any      ,		_A   :  Dict):
											A__						:				List[Any]         =			JukeboxTokenizer.from_pretrained(lowercase__)
											A__						:				int         =			tokenizer(**self.metas)["input_ids"]
											return tokens







					@slow
					def 			_lowercase      (							self   :  Dict):
											A__						:				List[str]         =			JukeboxModel.from_pretrained(self.model_id      ,		min_duration=0).eval()
											A__						:				Optional[int]         =			self.prepare_inputs(self.model_id)

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

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

											set_seed(0)
											A__						:				List[str]         =			model._sample(lowercase__      ,		lowercase__      ,		[2]      ,		sample_length=60 * model.priors[2].raw_to_tokens      ,		save_results=lowercase__)
											self.assertIn(zs[2][0].detach().cpu().tolist()      ,		[self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2])







					@slow
					@skip("Not enough GPU memory on CI runners")
					def 			_lowercase      (							self   :  List[Any]):
											A__						:				Optional[Any]         =			JukeboxModel.from_pretrained(self.model_id      ,		min_duration=0).eval()
											A__						:				int         =			[i.cuda() for i in self.prepare_inputs(self.model_id)]

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

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

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







					@slow
					def 			_lowercase      (							self   :  Any):
											A__						:				Optional[Any]         =			"ArthurZ/jukebox_prior_0"
											A__						:				Optional[int]         =			JukeboxPrior.from_pretrained(lowercase__      ,		min_duration=0).eval().half().to("cuda")

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







import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize





class        a__      :






		def __init__(      self							, lowercase__							, lowercase__=sys.maxsize						)				->				str:
				__A    =							"bilinear"
				__A    =							max_size
				__A    =							short_edge_length






		def __call__(      self							, lowercase__						)				->				Dict:
				__A    =							[]
				for img in imgs:
						__A ,   __A    =							img.shape[:2]
						# later: provide list and randomly choose index for resize
						__A    =							np.random.randint(self.short_edge_length[0]							, self.short_edge_length[1] + 1						)
						if size == 0:
								return img
						__A    =							size * 1.0 / min(lowercase__							, lowercase__						)
						if h < w:
								__A ,   __A    =							size, scale * w
						else:
								__A ,   __A    =							scale * h, size
						if max(lowercase__							, lowercase__						) > self.max_size:
								__A    =							self.max_size * 1.0 / max(lowercase__							, lowercase__						)
								__A    =							newh * scale
								__A    =							neww * scale
						__A    =							int(neww + 0.5						)
						__A    =							int(newh + 0.5						)

						if img.dtype == np.uinta:
								__A    =							Image.fromarray(lowercase__						)
								__A    =							pil_image.resize((neww, newh)							, PILImageResampling.BILINEAR						)
								__A    =							np.asarray(lowercase__						)
						else:
								__A    =							img.permute(2							, 0							, 1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
								__A    =							nn.functional.interpolate(
								    lowercase__							, (newh, neww)							, mode=self.interp_method							, align_corners=lowercase__						).squeeze(0						)
						img_augs.append(lowercase__						)

				return img_augs





class        a__      :






		def __init__(      self							, lowercase__						)				->				Union[str, Any]:
				__A    =							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]							, cfg.INPUT.MAX_SIZE_TEST						)
				__A    =							cfg.INPUT.FORMAT
				__A    =							cfg.SIZE_DIVISIBILITY
				__A    =							cfg.PAD_VALUE
				__A    =							cfg.INPUT.MAX_SIZE_TEST
				__A    =							cfg.MODEL.DEVICE
				__A    =							torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)							, 1							, 1						)
				__A    =							torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)							, 1							, 1						)
				__A    =							lambda lowercase__						: (x - self.pixel_mean) / self.pixel_std






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				Optional[int]:
				__A    =							tuple(max(lowercase__						) for s in zip(*[img.shape for img in images]						)						)
				__A    =							[im.shape[-2:] for im in images]
				__A    =							[
				    nn.functional.pad(
				        lowercase__							, [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]							, value=self.pad_value							, )
				    for size, im in zip(lowercase__							, lowercase__						)
				]

				return torch.stack(lowercase__						), torch.tensor(lowercase__						)






		def __call__(      self							, lowercase__							, lowercase__=False						)				->				int:
				with torch.no_grad():
						if not isinstance(lowercase__							, lowercase__						):
								__A    =							[images]
						if single_image:
								assert len(lowercase__						) == 1
						for i in range(len(lowercase__						)						):
								if isinstance(images[i]							, torch.Tensor						):
										images.insert(lowercase__							, images.pop(lowercase__						).to(self.device						).float()						)
								elif not isinstance(images[i]							, torch.Tensor						):
										images.insert(
										    lowercase__							, torch.as_tensor(img_tensorize(images.pop(lowercase__						)							, input_format=self.input_format						)						)
										    .to(self.device						)
										    .float()							, )
            # resize smallest edge
						__A    =							torch.tensor([im.shape[:2] for im in images]						)
						__A    =							self.aug(lowercase__						)
						# transpose images and convert to torch tensors
						# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
						# now normalize before pad to avoid useless arithmetic
						__A    =							[self.normalizer(lowercase__						) for x in images]
						# now pad them to do the following operations
						__A ,   __A    =							self.pad(lowercase__						)
						# Normalize

						if self.size_divisibility > 0:
								raise NotImplementedError()
						# pad
						__A    =							torch.true_divide(lowercase__							, lowercase__						)
						if single_image:
								return images[0], sizes[0], scales_yx[0]
						else:
								return images, sizes, scales_yx



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes





def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		assert torch.isfinite(lowerCAmelCase__	).all(), "Box tensor contains infinite or NaN!"
		__A ,   __A    =							box_size
		tensor[:, 0].clamp_(min=0					,     max=lowerCAmelCase__	)
		tensor[:, 1].clamp_(min=0					,     max=lowerCAmelCase__	)
		tensor[:, 2].clamp_(min=0					,     max=lowerCAmelCase__	)
		tensor[:, 3].clamp_(min=0					,     max=lowerCAmelCase__	)

