





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     ):
				__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,
    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
				__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  =					[
				    -0.0_4_9_2, -0.0_5_2_4, -0.0_5_6_5, -0.0_6_4_0, -0.0_6_8_6, -0.0_6_8_4, -0.0_6_7_7, -0.0_6_6_4,
				    -0.0_6_0_5, -0.0_4_9_0, -0.0_3_3_0, -0.0_1_6_8, -0.0_0_8_3, -0.0_0_7_5, -0.0_0_5_1, 0.0_0_2_5,
				    0.0_1_3_6, 0.0_2_6_1, 0.0_3_8_6, 0.0_4_9_7, 0.0_5_8_0, 0.0_5_9_9, 0.0_5_8_3, 0.0_6_1_4,
				    0.0_7_4_0, 0.0_8_8_9, 0.1_0_2_3, 0.1_1_6_2, 0.1_2_1_1, 0.1_2_1_2, 0.1_2_5_1, 0.1_3_3_6,
				    0.1_5_0_2, 0.1_6_8_6, 0.1_8_8_3, 0.2_1_4_8, 0.2_3_6_3, 0.2_4_5_8, 0.2_5_0_7, 0.2_5_3_1
				]
				__A  =					[
				    0.0_2_5_6, -0.0_5_4_4, 0.1_6_0_0, -0.0_0_3_2, 0.1_0_6_6, 0.0_8_2_5, -0.0_0_1_3, 0.3_4_4_0,
				    0.0_2_1_0, 0.0_4_1_2, -0.1_7_7_7, -0.0_8_9_2, -0.0_1_6_4, 0.0_2_8_5, -0.0_6_1_3, -0.0_6_1_7,
				    -0.0_1_3_7, -0.0_2_0_1, -0.0_1_7_5, 0.0_2_1_5, -0.0_6_2_7, 0.0_5_2_0, -0.0_7_3_0, 0.0_9_7_0,
				    -0.0_1_0_0, 0.0_4_4_2, -0.0_5_8_6, 0.0_2_0_7, -0.0_0_1_5, -0.0_0_8_2
				]
				__A  =					[
				    0.0_4_1_5, 0.0_8_7_7, 0.0_0_2_2, -0.0_0_5_5, 0.0_7_5_1, 0.0_3_3_4, 0.0_3_2_4, -0.0_0_6_8,
				    0.0_0_1_1, 0.0_0_1_7, -0.0_6_7_6, 0.0_6_5_5, -0.0_1_4_3, 0.0_3_9_9, 0.0_3_0_3, 0.0_7_4_3,
				    -0.0_1_6_8, -0.0_3_9_4, -0.1_1_1_3, 0.0_1_2_4, 0.0_4_4_2, 0.0_2_6_7, -0.0_0_0_3, -0.1_5_3_6,
				    -0.0_1_1_6, -0.1_8_3_7, -0.0_1_8_0, -0.1_0_2_6, -0.0_7_7_7, -0.0_4_5_6
				]
				__A  =					[
				    76, 27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45, 45, 76, 38, 31, 33,
				    45, 76, 41, 32, 76, 45, 46, 41, 40, 31, 78, 76
				]


				def _snake_case							(		self				):
								lowerCamelCase									=JukeboxTokenizer.from_pretrained(self.model_id				)
								lowerCamelCase									=tokenizer(**self.metas				)["""input_ids"""]
								return tokens


				@slow
				def _snake_case							(		self				):
								lowerCamelCase									=JukeboxModel.from_pretrained(self.model_id ,			min_duration=0				).eval()
								lowerCamelCase									=self.prepare_inputs()

								set_seed(0				)
								lowerCamelCase									=[torch.zeros(1 ,			0 ,			dtype=torch.long				).cpu() for _ in range(3				)]
								lowerCamelCase									=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				)
								lowerCamelCase									=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				)
								lowerCamelCase									=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 _snake_case							(		self				):
								lowerCamelCase									=False
								lowerCamelCase									=JukeboxModel.from_pretrained(self.model_id ,			min_duration=0				).eval()

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

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

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

								lowerCamelCase			,	lowerCamelCase			,	lowerCamelCase									=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 _snake_case							(		self				):
								lowerCamelCase									=False

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

								lowerCamelCase									=[model.vqvae.encode(UpperCAmelCase_ ,			start_level=2 ,			bs_chunks=waveform.shape[0]				)[0], None, None]
								lowerCamelCase									=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				)				)

								lowerCamelCase									=torch.cat((zs[0], torch.zeros(1 ,			2048 - zs[0].shape[-1]				)) ,			dim=-1				).long()
								lowerCamelCase									=[upper_a, model.vqvae.encode(UpperCAmelCase_ ,			start_level=1 ,			bs_chunks=waveform.shape[0]				)[0], None]
								lowerCamelCase									=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				)				)

								lowerCamelCase									=torch.cat((zs[1], torch.zeros(1 ,			2048 - zs[1].shape[-1]				)) ,			dim=-1				).long()
								lowerCamelCase									=[upper_a, upper_a, model.vqvae.encode(UpperCAmelCase_ ,			start_level=0 ,			bs_chunks=waveform.shape[0]				)[0]]
								lowerCamelCase									=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 _snake_case							(		self				):
								lowerCamelCase									=JukeboxModel.from_pretrained(self.model_id ,			min_duration=0				).eval()
								set_seed(0				)
								lowerCamelCase									=torch.rand((1, 5120, 1)				)
								with torch.no_grad():
												lowerCamelCase									=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():
												lowerCamelCase									=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     ):
				__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,
    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
				__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 _snake_case							(		self ,			UpperCAmelCase_				):
								lowerCamelCase									=JukeboxTokenizer.from_pretrained(UpperCAmelCase_				)
								lowerCamelCase									=tokenizer(**self.metas				)["""input_ids"""]
								return tokens


				@slow
				def _snake_case							(		self				):
								lowerCamelCase									=JukeboxModel.from_pretrained(self.model_id ,			min_duration=0				).eval()
								lowerCamelCase									=self.prepare_inputs(self.model_id				)

								set_seed(0				)
								lowerCamelCase									=[torch.zeros(1 ,			0 ,			dtype=torch.long				).cpu() for _ in range(3				)]
								lowerCamelCase									=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				)
								lowerCamelCase									=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				)
								lowerCamelCase									=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 _snake_case							(		self				):
								lowerCamelCase									=JukeboxModel.from_pretrained(self.model_id ,			min_duration=0				).eval()
								lowerCamelCase									=[i.cuda() for i in self.prepare_inputs(self.model_id				)]

								set_seed(0				)
								model.priors[0].cuda()
								lowerCamelCase									=[torch.zeros(1 ,			0 ,			dtype=torch.long				).cuda() for _ in range(3				)]
								lowerCamelCase									=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()
								lowerCamelCase									=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()
								lowerCamelCase									=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 _snake_case							(		self				):
								lowerCamelCase									="""ArthurZ/jukebox_prior_0"""
								lowerCamelCase									=JukeboxPrior.from_pretrained(UpperCAmelCase_ ,			min_duration=0				).eval().half().to("""cuda"""				)

								lowerCamelCase									=self.prepare_inputs(UpperCAmelCase_				)[0].cuda()
								lowerCamelCase									=model.get_metadata(UpperCAmelCase_ ,			0 ,			7680 ,			0				)
								set_seed(0				)
								lowerCamelCase									=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]				)












def   _lowercase       (    _UpperCAmelCase					)    ->	bool:
				if not isinstance(_UpperCAmelCase      ,   _UpperCAmelCase					):
								lowerCamelCase									=F"""Input value of [number={number}] must be an integer"""
								raise TypeError(_UpperCAmelCase					)
				if number < 0 or number % 2 == 1:
								return False
				lowerCamelCase									=int(number**0.5					)
				return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()





