
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    _UpperCamelCase(   unittest.TestCase				):
						__A:						str				   =      (JukeboxModel,) if is_torch_available() else ()
						__A:						List[str]				   =      "openai/jukebox-1b-lyrics"
						__A:						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
						__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:						List[str]				   =      [
						    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:						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
						]

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

						__A:						Union[str, 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:						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:						int				   =      [
						    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:						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:						List[Any]				   =      [
						    -0.04_92, -0.05_24, -0.05_65, -0.06_40, -0.06_86, -0.06_84, -0.06_77, -0.06_64,
						    -0.06_05, -0.04_90, -0.03_30, -0.01_68, -0.00_83, -0.00_75, -0.00_51, 0.00_25,
						    0.01_36, 0.02_61, 0.03_86, 0.04_97, 0.05_80, 0.05_99, 0.05_83, 0.06_14,
						    0.07_40, 0.08_89, 0.10_23, 0.11_62, 0.12_11, 0.12_12, 0.12_51, 0.13_36,
						    0.15_02, 0.16_86, 0.18_83, 0.21_48, 0.23_63, 0.24_58, 0.25_07, 0.25_31
						]
						__A:						List[str]				   =      [
						    0.02_56, -0.05_44, 0.16_00, -0.00_32, 0.10_66, 0.08_25, -0.00_13, 0.34_40,
						    0.02_10, 0.04_12, -0.17_77, -0.08_92, -0.01_64, 0.02_85, -0.06_13, -0.06_17,
						    -0.01_37, -0.02_01, -0.01_75, 0.02_15, -0.06_27, 0.05_20, -0.07_30, 0.09_70,
						    -0.01_00, 0.04_42, -0.05_86, 0.02_07, -0.00_15, -0.00_82
						]
						__A:						int				   =      [
						    0.04_15, 0.08_77, 0.00_22, -0.00_55, 0.07_51, 0.03_34, 0.03_24, -0.00_68,
						    0.00_11, 0.00_17, -0.06_76, 0.06_55, -0.01_43, 0.03_99, 0.03_03, 0.07_43,
						    -0.01_68, -0.03_94, -0.11_13, 0.01_24, 0.04_42, 0.02_67, -0.00_03, -0.15_36,
						    -0.01_16, -0.18_37, -0.01_80, -0.10_26, -0.07_77, -0.04_56
						]
						__A:						Dict				   =      [
						    76, 27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45, 45, 76, 38, 31, 33,
						    45, 76, 41, 32, 76, 45, 46, 41, 40, 31, 78, 76
						]

						def 						a__ (					self		:			Dict			):
													_UpperCAmelCase  :			Dict	       =		JukeboxTokenizer.from_pretrained(self.model_id			)
													_UpperCAmelCase  :			List[Any]	       =		tokenizer(**self.metas			)["input_ids"]
													return tokens

						@slow
						def 						a__ (					self		:			Dict			):
													_UpperCAmelCase  :			List[Any]	       =		JukeboxModel.from_pretrained(self.model_id	,						min_duration=0			).eval()
													_UpperCAmelCase  :			Tuple	       =		self.prepare_inputs()

													set_seed(0			)
													_UpperCAmelCase  :			Union[str, Any]	       =		[torch.zeros(1	,						0	,						dtype=torch.long			).cpu() for _ in range(3			)]
													_UpperCAmelCase  :			Union[str, Any]	       =		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			)
													_UpperCAmelCase  :			Optional[Any]	       =		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			)
													_UpperCAmelCase  :			Dict	       =		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 						a__ (					self		:			Optional[int]			):
													_UpperCAmelCase  :			Any	       =		False
													_UpperCAmelCase  :			Tuple	       =		JukeboxModel.from_pretrained(self.model_id	,						min_duration=0			).eval()

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

													_UpperCAmelCase  :			List[Any]	       =		model.priors[0]
													_UpperCAmelCase  :			List[Any]	       =		0
													_UpperCAmelCase  :			str	       =		top_prior.get_music_tokens_conds(_UpperCAmelCase	,						start=_UpperCAmelCase	,						end=start + top_prior.n_ctx			)
													_UpperCAmelCase  :			str	       =		top_prior.get_metadata(labels[0].clone()	,						_UpperCAmelCase	,						1_05_83_04	,						0			)

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

													_UpperCAmelCase       ,_UpperCAmelCase       ,_UpperCAmelCase  :			Optional[Any]	       =		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 						a__ (					self		:			Union[str, Any]			):
													_UpperCAmelCase  :			Union[str, Any]	       =		False

													_UpperCAmelCase  :			Union[str, Any]	       =		JukeboxModel.from_pretrained(self.model_id	,						min_duration=0			).eval()
													set_seed(0			)
													_UpperCAmelCase  :			Tuple	       =		torch.rand((1, 51_20, 1)			)
													_UpperCAmelCase  :			Optional[Any]	       =		list(self.prepare_inputs()			)

													_UpperCAmelCase  :			Any	       =		[model.vqvae.encode(_UpperCAmelCase	,						start_level=2	,						bs_chunks=waveform.shape[0]			)[0], None, None]
													_UpperCAmelCase  :			Union[str, Any]	       =		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			)			)

													_UpperCAmelCase  :			Union[str, Any]	       =		torch.cat((zs[0], torch.zeros(1	,						20_48 - zs[0].shape[-1]			))	,						dim=-1			).long()
													_UpperCAmelCase  :			Union[str, Any]	       =		[upper_a, model.vqvae.encode(_UpperCAmelCase	,						start_level=1	,						bs_chunks=waveform.shape[0]			)[0], None]
													_UpperCAmelCase  :			Dict	       =		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			)			)

													_UpperCAmelCase  :			Any	       =		torch.cat((zs[1], torch.zeros(1	,						20_48 - zs[1].shape[-1]			))	,						dim=-1			).long()
													_UpperCAmelCase  :			str	       =		[upper_a, upper_a, model.vqvae.encode(_UpperCAmelCase	,						start_level=0	,						bs_chunks=waveform.shape[0]			)[0]]
													_UpperCAmelCase  :			Dict	       =		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 						a__ (					self		:			List[str]			):
													_UpperCAmelCase  :			str	       =		JukeboxModel.from_pretrained(self.model_id	,						min_duration=0			).eval()
													set_seed(0			)
													_UpperCAmelCase  :			Optional[int]	       =		torch.rand((1, 51_20, 1)			)
													with torch.no_grad():
																				_UpperCAmelCase  :			List[Any]	       =		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():
																				_UpperCAmelCase  :			List[str]	       =		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    _UpperCamelCase(   unittest.TestCase				):
						__A:						List[Any]				   =      (JukeboxModel,) if is_torch_available() else ()
						__A:						int				   =      "openai/jukebox-5b-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:						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, 6_53, 6_53, 6_53, 6_53, 6_53,
						    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:						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:						Union[str, Any]				   =      [
						    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:						Tuple				   =      [
						    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:						Any				   =      [
						    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:						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, 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:						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:						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:						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
						]
						__A:						int				   =      [
						    4_91, 17_55, 34, 16_13, 17_55, 4_17, 9_92, 16_13, 2_22, 8_42, 13_53, 16_13,
						    8_44, 6_32, 1_85, 16_13, 8_44, 6_32, 1_85, 16_13, 1_85, 8_42, 6_77, 16_13,
						    1_85, 1_14, 13_53, 16_13, 3_07, 89, 8_44, 16_13, 3_07, 13_32, 2_34, 19_79,
						    3_07, 89, 13_53, 6_16, 34, 8_42, 1_85, 8_42, 34, 8_42, 1_85, 8_42,
						    3_07, 1_14, 1_85, 89, 34, 12_68, 1_85, 89, 34, 8_42, 1_85, 89
						]

						def 						a__ (					self		:			Optional[int]	,						_lowerCamelCase		:			List[str]			):
													_UpperCAmelCase  :			Optional[Any]	       =		JukeboxTokenizer.from_pretrained(_UpperCAmelCase			)
													_UpperCAmelCase  :			Dict	       =		tokenizer(**self.metas			)["input_ids"]
													return tokens

						@slow
						def 						a__ (					self		:			List[Any]			):
													_UpperCAmelCase  :			int	       =		JukeboxModel.from_pretrained(self.model_id	,						min_duration=0			).eval()
													_UpperCAmelCase  :			str	       =		self.prepare_inputs(self.model_id			)

													set_seed(0			)
													_UpperCAmelCase  :			Optional[Any]	       =		[torch.zeros(1	,						0	,						dtype=torch.long			).cpu() for _ in range(3			)]
													_UpperCAmelCase  :			Tuple	       =		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			)
													_UpperCAmelCase  :			Dict	       =		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			)
													_UpperCAmelCase  :			Dict	       =		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 						a__ (					self		:			int			):
													_UpperCAmelCase  :			Optional[Any]	       =		JukeboxModel.from_pretrained(self.model_id	,						min_duration=0			).eval()
													_UpperCAmelCase  :			List[str]	       =		[i.cuda() for i in self.prepare_inputs(self.model_id			)]

													set_seed(0			)
													model.priors[0].cuda()
													_UpperCAmelCase  :			Union[str, Any]	       =		[torch.zeros(1	,						0	,						dtype=torch.long			).cuda() for _ in range(3			)]
													_UpperCAmelCase  :			Union[str, Any]	       =		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()
													_UpperCAmelCase  :			List[str]	       =		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()
													_UpperCAmelCase  :			str	       =		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 						a__ (					self		:			Union[str, Any]			):
													_UpperCAmelCase  :			Any	       =		"ArthurZ/jukebox_prior_0"
													_UpperCAmelCase  :			int	       =		JukeboxPrior.from_pretrained(_UpperCAmelCase	,						min_duration=0			).eval().half().to("cuda"			)

													_UpperCAmelCase  :			List[str]	       =		self.prepare_inputs(_UpperCAmelCase			)[0].cuda()
													_UpperCAmelCase  :			Union[str, Any]	       =		model.get_metadata(_UpperCAmelCase	,						0	,						76_80	,						0			)
													set_seed(0			)
													_UpperCAmelCase  :			List[Any]	       =		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]			)






import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]     )	->		str:
				__lowercase								=   filter(lambda SCREAMING_SNAKE_CASE     : p.requires_grad       ,				model.parameters()     )
				__lowercase								=   sum([np.prod(p.size()     ) for p in model_parameters]     )
				return params


SCREAMING_SNAKE_CASE__				=							logging.getLogger(__name__)
class       A__  (		pl.Callback							):





							def      a__					(					self    :  List[str]	,					_UpperCAmelCase    :  Tuple	,					_UpperCAmelCase    :  List[str]							)		->						Tuple:



											"""simple docstring"""


											__lowercase								=   {f"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
											pl_module.logger.log_metrics(_UpperCAmelCase							)





							@rank_zero_only
							def      a__					(					self    :  Dict	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule	,					_UpperCAmelCase    :  str	,					_UpperCAmelCase    :  int=True							)		->						None:



											"""simple docstring"""


											logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""							)
											__lowercase								=   trainer.callback_metrics
											trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}							)
											# Log results
											__lowercase								=   Path(pl_module.hparams.output_dir							)
											if type_path == "test":
															__lowercase								=   od / 'test_results.txt'
															__lowercase								=   od / 'test_generations.txt'
											else:
															# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
															# If people want this it will be easy enough to add back.
															__lowercase								=   od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
															__lowercase								=   od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
															results_file.parent.mkdir(exist_ok=_UpperCAmelCase							)
															generations_file.parent.mkdir(exist_ok=_UpperCAmelCase							)
											with open(_UpperCAmelCase	,					'a+'							) as writer:
															for key in sorted(_UpperCAmelCase							):
																			if key in ["log", "progress_bar", "preds"]:
																							continue
																			__lowercase								=   metrics[key]
																			if isinstance(_UpperCAmelCase	,					torch.Tensor							):
																							__lowercase								=   val.item()
																			__lowercase								=   f"""{key}: {val:.6f}\n"""
																			writer.write(_UpperCAmelCase							)

											if not save_generations:
															return

											if "preds" in metrics:
															__lowercase								=   '\n'.join(metrics['preds']							)
															generations_file.open('w+'							).write(_UpperCAmelCase							)





							@rank_zero_only
							def      a__					(					self    :  str	,					_UpperCAmelCase    :  Optional[int]	,					_UpperCAmelCase    :  str							)		->						Optional[int]:



											"""simple docstring"""


											try:
															__lowercase								=   pl_module.model.model.num_parameters()
											except AttributeError:
															__lowercase								=   pl_module.model.num_parameters()

											__lowercase								=   count_trainable_parameters(_UpperCAmelCase							)
											# mp stands for million parameters
											trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6}							)





							@rank_zero_only
							def      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule							)		->						Optional[Any]:



											"""simple docstring"""


											save_json(pl_module.metrics	,					pl_module.metrics_save_path							)
											return self._write_logs(_UpperCAmelCase	,					_UpperCAmelCase	,					'test'							)





							@rank_zero_only
							def      a__					(					self    :  int	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  Tuple							)		->						str:



											"""simple docstring"""


											save_json(pl_module.metrics	,					pl_module.metrics_save_path							)
											# Uncommenting this will save val generations
											# return self._write_logs(trainer, pl_module, "valid")






def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: List[str]       ,				SCREAMING_SNAKE_CASE			: Union[str, Any]=1       ,				SCREAMING_SNAKE_CASE			: str=False     )	->		List[str]:
				if metric == "rouge2":
								__lowercase								=   '{val_avg_rouge2:.4f}-{step_count}'
				elif metric == "bleu":
								__lowercase								=   '{val_avg_bleu:.4f}-{step_count}'
				elif metric == "loss":
								__lowercase								=   '{val_avg_loss:.4f}-{step_count}'
				else:
								raise NotImplementedError(
								    F"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
								    ' this function.'     )

				__lowercase								=   ModelCheckpoint(
				    dirpath=SCREAMING_SNAKE_CASE       ,				filename=SCREAMING_SNAKE_CASE       ,				monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				save_top_k=SCREAMING_SNAKE_CASE       ,				)
				return checkpoint_callback




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any       ,				SCREAMING_SNAKE_CASE			: int     )	->		Union[str, Any]:
				return EarlyStopping(
				    monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				patience=SCREAMING_SNAKE_CASE       ,				verbose=SCREAMING_SNAKE_CASE       ,				)





