






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		):
		UpperCAmelCase_							:       Union[str, Any] 					= (JukeboxModel,) if is_torch_available() else ()
		UpperCAmelCase_							:       str 					= 'openai/jukebox-1b-lyrics'
		UpperCAmelCase_							:       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
		UpperCAmelCase_							:       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
		]

		UpperCAmelCase_							:       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
		]

		UpperCAmelCase_							:       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
		]
		UpperCAmelCase_							:       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
		]

		UpperCAmelCase_							:       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
		]
		UpperCAmelCase_							:       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
		]

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

		UpperCAmelCase_							:       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
		]
		UpperCAmelCase_							:       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
		]
		UpperCAmelCase_							:       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
		]
		UpperCAmelCase_							:       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
		]
		UpperCAmelCase_							:       Any 					= [
		    -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
		]
		UpperCAmelCase_							:       Any 					= [
		    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
		]
		UpperCAmelCase_							:       Union[str, Any] 					= [
		    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
		]
		UpperCAmelCase_							:       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 					_lowerCamelCase					(      self						)				->				Dict:
				__A    =							JukeboxTokenizer.from_pretrained(self.model_id						)
				__A    =							tokenizer(**self.metas						)["input_ids"]
				return tokens






		@slow
		def 					_lowerCamelCase					(      self						)				->				int:
				__A    =							JukeboxModel.from_pretrained(self.model_id							, min_duration=0						).eval()
				__A    =							self.prepare_inputs()

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

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

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

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

				__A ,   __A ,   __A    =							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 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							False

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

				__A    =							[model.vqvae.encode(lowercase__							, start_level=2							, bs_chunks=waveform.shape[0]						)[0], None, None]
				__A    =							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    =							torch.cat((zs[0], torch.zeros(1							, 2048 - zs[0].shape[-1]						))							, dim=-1						).long()
				__A    =							[upper_a, model.vqvae.encode(lowercase__							, start_level=1							, bs_chunks=waveform.shape[0]						)[0], None]
				__A    =							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    =							torch.cat((zs[1], torch.zeros(1							, 2048 - zs[1].shape[-1]						))							, dim=-1						).long()
				__A    =							[upper_a, upper_a, model.vqvae.encode(lowercase__							, start_level=0							, bs_chunks=waveform.shape[0]						)[0]]
				__A    =							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 					_lowerCamelCase					(      self						)				->				Any:
				__A    =							JukeboxModel.from_pretrained(self.model_id							, min_duration=0						).eval()
				set_seed(0						)
				__A    =							torch.rand((1, 5120, 1)						)
				with torch.no_grad():
						__A    =							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    =							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        a__      (	unittest.TestCase		):
		UpperCAmelCase_							:       List[Any] 					= (JukeboxModel,) if is_torch_available() else ()
		UpperCAmelCase_							:       List[Any] 					= 'openai/jukebox-5b-lyrics'
		UpperCAmelCase_							:       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
		UpperCAmelCase_							:       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
		]
		UpperCAmelCase_							:       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
		]

		UpperCAmelCase_							:       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
		]
		UpperCAmelCase_							:       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
		]

		UpperCAmelCase_							:       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
		]
		UpperCAmelCase_							:       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
		]

		UpperCAmelCase_							:       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
		]
		UpperCAmelCase_							:       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
		]

		UpperCAmelCase_							:       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
		]
		UpperCAmelCase_							:       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 					_lowerCamelCase					(      self							, lowercase__						)				->				str:
				__A    =							JukeboxTokenizer.from_pretrained(lowercase__						)
				__A    =							tokenizer(**self.metas						)["input_ids"]
				return tokens






		@slow
		def 					_lowerCamelCase					(      self						)				->				int:
				__A    =							JukeboxModel.from_pretrained(self.model_id							, min_duration=0						).eval()
				__A    =							self.prepare_inputs(self.model_id						)

				set_seed(0						)
				__A    =							[torch.zeros(1							, 0							, dtype=torch.long						).cpu() for _ in range(3						)]
				__A    =							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    =							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    =							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 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							JukeboxModel.from_pretrained(self.model_id							, min_duration=0						).eval()
				__A    =							[i.cuda() for i in self.prepare_inputs(self.model_id						)]

				set_seed(0						)
				model.priors[0].cuda()
				__A    =							[torch.zeros(1							, 0							, dtype=torch.long						).cuda() for _ in range(3						)]
				__A    =							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    =							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    =							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 					_lowerCamelCase					(      self						)				->				Any:
				__A    =							"ArthurZ/jukebox_prior_0"
				__A    =							JukeboxPrior.from_pretrained(lowercase__							, min_duration=0						).eval().half().to("cuda"						)

				__A    =							self.prepare_inputs(lowercase__						)[0].cuda()
				__A    =							model.get_metadata(lowercase__							, 0							, 7680							, 0						)
				set_seed(0						)
				__A    =							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 shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
       from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


_UpperCamelCase:					str     =get_tests_dir('fixtures/test_sentencepiece.model')





@require_torch
@require_torchaudio
@require_sentencepiece
class 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Any:
         _lowerCAmelCase						=		tempfile.mkdtemp()

         _lowerCAmelCase						=		['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
         _lowerCAmelCase						=		dict(zip(_lowerCAmelCase  ,      range(len(_lowerCAmelCase       )       )       )       )
         _lowerCAmelCase						=		Path(self.tmpdirname       )
         save_json(_lowerCAmelCase  ,      save_dir / VOCAB_FILES_NAMES['vocab_file']       )
         if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
           copyfile(_lowerCAmelCase  ,      save_dir / VOCAB_FILES_NAMES['spm_file']       )

         _lowerCAmelCase						=		SpeechaTextTokenizer.from_pretrained(self.tmpdirname       )
         tokenizer.save_pretrained(self.tmpdirname       )

         _lowerCAmelCase						=		{
             'feature_size': 24,
             'num_mel_bins': 24,
             'padding_value': 0.0,
             'sampling_rate': 1_6000,
             'return_attention_mask': False,
             'do_normalize': True,
         }
         save_json(_lowerCAmelCase  ,      save_dir / FEATURE_EXTRACTOR_NAME       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      **_lowerCAmelCase					:			Optional[Any]       )					->   List[str]:
         return SpeechaTextTokenizer.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      **_lowerCAmelCase					:			Dict       )					->   Tuple:
         return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   int:
         shutil.rmtree(self.tmpdirname       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Any:
         _lowerCAmelCase						=		self.get_tokenizer()
         _lowerCAmelCase						=		self.get_feature_extractor()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         processor.save_pretrained(self.tmpdirname       )
         _lowerCAmelCase						=		SpeechaTextProcessor.from_pretrained(self.tmpdirname       )

         self.assertEqual(processor.tokenizer.get_vocab()  ,      tokenizer.get_vocab()       )
         self.assertIsInstance(processor.tokenizer  ,      _lowerCAmelCase       )

         self.assertEqual(processor.feature_extractor.to_json_string()  ,      feature_extractor.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   int:
         _lowerCAmelCase						=		SpeechaTextProcessor(
             tokenizer=self.get_tokenizer()  ,      feature_extractor=self.get_feature_extractor()       )
         processor.save_pretrained(self.tmpdirname       )

         _lowerCAmelCase						=		self.get_tokenizer(bos_token='(BOS)'  ,      eos_token='(EOS)'       )
         _lowerCAmelCase						=		self.get_feature_extractor(do_normalize=_lowerCAmelCase  ,      padding_value=1.0       )

         _lowerCAmelCase						=		SpeechaTextProcessor.from_pretrained(
             self.tmpdirname  ,      bos_token='(BOS)'  ,      eos_token='(EOS)'  ,      do_normalize=_lowerCAmelCase  ,      padding_value=1.0       )

         self.assertEqual(processor.tokenizer.get_vocab()  ,      tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer  ,      _lowerCAmelCase       )

         self.assertEqual(processor.feature_extractor.to_json_string()  ,      feature_extractor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		floats_list((3, 1000)       )

         _lowerCAmelCase						=		feature_extractor(_lowerCAmelCase  ,      return_tensors='np'       )
         _lowerCAmelCase						=		processor(_lowerCAmelCase  ,      return_tensors='np'       )

         for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()  ,      input_processor[key].sum()  ,      delta=1e-2       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Any:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		'This is a test string'

         _lowerCAmelCase						=		processor(text=_lowerCAmelCase       )

         _lowerCAmelCase						=		tokenizer(_lowerCAmelCase       )

         for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]  ,      encoded_processor[key]       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   Tuple:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         _lowerCAmelCase						=		processor.batch_decode(_lowerCAmelCase       )
         _lowerCAmelCase						=		tokenizer.batch_decode(_lowerCAmelCase       )

         self.assertListEqual(_lowerCAmelCase  ,      _lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         self.assertListEqual(
             processor.model_input_names  ,      feature_extractor.model_input_names  ,      msg='`processor` and `feature_extractor` model input names do not match'  ,      )
