


'''simple docstring'''






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      SCREAMING_SNAKE_CASE_	( unittest.TestCase		):
			__a					:      List[str]					=  (JukeboxModel,) if is_torch_available() else ()
			__a					:      Optional[int]					=  '''openai/jukebox-1b-lyrics'''
			__a					:      Any					=  {
			    '''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					:      Union[str, 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					:      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
			]

			__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					:      str					=  [
			    4_16, 4_16, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
			    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
			    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
			    4_16, 4_16, 4_16, 4_16
			]

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

			__a					:      Tuple					=  [
			    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[int]					=  [
			    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					:      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[int]					=  [
			    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					:      List[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					:      List[str]					=  [
			    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					:      Tuple					=  [
			    76, 27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45, 45, 76, 38, 31, 33,
			    45, 76, 41, 32, 76, 45, 46, 41, 40, 31, 78, 76
			]




			def 			_snake_case   (      self       )			->						Tuple:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							List[str]			   = JukeboxTokenizer.from_pretrained(self.model_id       )
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = tokenizer(**self.metas       )['''input_ids''']
								return tokens




			@slow
			def 			_snake_case   (      self       )			->						List[Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Any			   = JukeboxModel.from_pretrained(self.model_id					,      min_duration=0       ).eval()
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = self.prepare_inputs()

								set_seed(0       )
								__SCREAMING_SNAKE_CASE					:							Tuple			   = [torch.zeros(1					,      0					,      dtype=torch.long       ).cpu() for _ in range(3       )]
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = model._sample(lowercase					,      lowercase					,      [0]					,      sample_length=4_0 * 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       )
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = model._sample(lowercase					,      lowercase					,      [1]					,      sample_length=4_0 * 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       )
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = model._sample(lowercase					,      lowercase					,      [2]					,      sample_length=4_0 * 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 			_snake_case   (      self       )			->						Optional[Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Tuple			   = False
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = JukeboxModel.from_pretrained(self.model_id					,      min_duration=0       ).eval()

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

								__SCREAMING_SNAKE_CASE					:							str			   = model.priors[0]
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = 0
								__SCREAMING_SNAKE_CASE					:							str			   = top_prior.get_music_tokens_conds(lowercase					,      start=lowercase					,      end=start + top_prior.n_ctx       )
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = top_prior.get_metadata(labels[0].clone()					,      lowercase					,      1_0_5_8_3_0_4					,      0       )

								self.assertIsNone(lowercase       )
								self.assertListEqual(metadata.numpy()[0][:1_0].tolist()					,      self.EXPECTED_Y_COND       )

								__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							int			   = top_prior.get_cond(lowercase					,      lowercase       )
								torch.testing.assert_allclose(
								    audio_conditioning[0][0][:3_0].detach()					,      torch.tensor(self.EXPECTED_AUDIO_COND       )					,      atol=1e-4					,      rtol=1e-4       )
								torch.testing.assert_allclose(
								    metadata_conditioning[0][0][:3_0].detach()					,      torch.tensor(self.EXPECTED_META_COND       )					,      atol=1e-4					,      rtol=1e-4       )
								torch.testing.assert_allclose(
								    lyric_tokens[0, :3_0].detach()					,      torch.tensor(self.EXPECTED_LYRIC_COND       )					,      atol=1e-4					,      rtol=1e-4       )




			@slow
			def 			_snake_case   (      self       )			->						int:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							int			   = False

								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = JukeboxModel.from_pretrained(self.model_id					,      min_duration=0       ).eval()
								set_seed(0       )
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = torch.rand((1, 5_1_2_0, 1)       )
								__SCREAMING_SNAKE_CASE					:							Tuple			   = list(self.prepare_inputs()       )

								__SCREAMING_SNAKE_CASE					:							str			   = [model.vqvae.encode(lowercase					,      start_level=2					,      bs_chunks=waveform.shape[0]       )[0], None, None]
								__SCREAMING_SNAKE_CASE					:							str			   = model._sample(
								    lowercase					,      lowercase					,      sample_levels=[0]					,      save_results=lowercase					,      sample_length=4_0 * model.priors[0].raw_to_tokens       )
								torch.testing.assert_allclose(zs[0][0][:4_0]					,      torch.tensor(self.EXPECTED_PRIMED_0       )       )

								__SCREAMING_SNAKE_CASE					:							Any			   = torch.cat((zs[0], torch.zeros(1					,      2_0_4_8 - zs[0].shape[-1]       ))					,      dim=-1       ).long()
								__SCREAMING_SNAKE_CASE					:							Any			   = [upper_a, model.vqvae.encode(lowercase					,      start_level=1					,      bs_chunks=waveform.shape[0]       )[0], None]
								__SCREAMING_SNAKE_CASE					:							str			   = model._sample(
								    lowercase					,      lowercase					,      sample_levels=[1]					,      save_results=lowercase					,      sample_length=4_0 * model.priors[1].raw_to_tokens       )
								torch.testing.assert_allclose(zs[1][0][:4_0]					,      torch.tensor(self.EXPECTED_PRIMED_1       )       )

								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = torch.cat((zs[1], torch.zeros(1					,      2_0_4_8 - zs[1].shape[-1]       ))					,      dim=-1       ).long()
								__SCREAMING_SNAKE_CASE					:							int			   = [upper_a, upper_a, model.vqvae.encode(lowercase					,      start_level=0					,      bs_chunks=waveform.shape[0]       )[0]]
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = model._sample(
								    lowercase					,      lowercase					,      sample_levels=[2]					,      save_results=lowercase					,      sample_length=4_0 * model.priors[2].raw_to_tokens       )
								torch.testing.assert_allclose(zs[2][0][:4_0].cpu()					,      torch.tensor(self.EXPECTED_PRIMED_2       )       )




			@slow
			def 			_snake_case   (      self       )			->						Optional[int]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							str			   = JukeboxModel.from_pretrained(self.model_id					,      min_duration=0       ).eval()
								set_seed(0       )
								__SCREAMING_SNAKE_CASE					:							Tuple			   = torch.rand((1, 5_1_2_0, 1)       )
								with torch.no_grad():
													__SCREAMING_SNAKE_CASE					:							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():
													__SCREAMING_SNAKE_CASE					:							List[str]			   = model.vqvae.decode(lowercase					,      start_level=2					,      bs_chunks=x.shape[0]       )
								torch.testing.assert_allclose(x[0, :4_0, 0]					,      torch.tensor(self.EXPECTED_VQVAE_DECODE       )					,      atol=1e-4					,      rtol=1e-4       )






@require_torch
class      SCREAMING_SNAKE_CASE_	( unittest.TestCase		):
			__a					:      Optional[Any]					=  (JukeboxModel,) if is_torch_available() else ()
			__a					:      int					=  '''openai/jukebox-5b-lyrics'''
			__a					:      List[Any]					=  {
			    '''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					:      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,
			    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[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, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
			    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]					=  [
			    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					:      str					=  [
			    4_16, 4_16, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
			    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
			    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
			    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 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					:      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					:      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, 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					:      Union[str, 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					:      Optional[int]					=  [
			    11_25, 11_25, 4_16, 11_25, 11_25, 4_16, 11_25, 11_25, 4_16, 4_16, 11_25, 4_16,
			    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
			    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
			    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
			    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
			]
			__a					:      Dict					=  [
			    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					,      lowercase       )			->						Tuple:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Tuple			   = JukeboxTokenizer.from_pretrained(lowercase       )
								__SCREAMING_SNAKE_CASE					:							List[str]			   = tokenizer(**self.metas       )['''input_ids''']
								return tokens




			@slow
			def 			_snake_case   (      self       )			->						Dict:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							str			   = JukeboxModel.from_pretrained(self.model_id					,      min_duration=0       ).eval()
								__SCREAMING_SNAKE_CASE					:							int			   = self.prepare_inputs(self.model_id       )

								set_seed(0       )
								__SCREAMING_SNAKE_CASE					:							Tuple			   = [torch.zeros(1					,      0					,      dtype=torch.long       ).cpu() for _ in range(3       )]
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = model._sample(lowercase					,      lowercase					,      [0]					,      sample_length=6_0 * 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       )
								__SCREAMING_SNAKE_CASE					:							int			   = model._sample(lowercase					,      lowercase					,      [1]					,      sample_length=6_0 * 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       )
								__SCREAMING_SNAKE_CASE					:							Dict			   = model._sample(lowercase					,      lowercase					,      [2]					,      sample_length=6_0 * 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 			_snake_case   (      self       )			->						Optional[Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Dict			   = JukeboxModel.from_pretrained(self.model_id					,      min_duration=0       ).eval()
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = [i.cuda() for i in self.prepare_inputs(self.model_id       )]

								set_seed(0       )
								model.priors[0].cuda()
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = [torch.zeros(1					,      0					,      dtype=torch.long       ).cuda() for _ in range(3       )]
								__SCREAMING_SNAKE_CASE					:							Tuple			   = model._sample(lowercase					,      lowercase					,      [0]					,      sample_length=6_0 * 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()
								__SCREAMING_SNAKE_CASE					:							List[str]			   = model._sample(lowercase					,      lowercase					,      [1]					,      sample_length=6_0 * 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()
								__SCREAMING_SNAKE_CASE					:							List[str]			   = model._sample(lowercase					,      lowercase					,      [2]					,      sample_length=6_0 * 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 			_snake_case   (      self       )			->						List[str]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = '''ArthurZ/jukebox_prior_0'''
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = JukeboxPrior.from_pretrained(lowercase					,      min_duration=0       ).eval().half().to('''cuda'''       )

								__SCREAMING_SNAKE_CASE					:							Dict			   = self.prepare_inputs(lowercase       )[0].cuda()
								__SCREAMING_SNAKE_CASE					:							Dict			   = model.get_metadata(lowercase					,      0					,      7_6_8_0					,      0       )
								set_seed(0       )
								__SCREAMING_SNAKE_CASE					:							Tuple			   = model.sample(1					,      metadata=lowercase					,      sample_tokens=6_0       )
								self.assertIn(outputs[0].cpu().tolist()					,      [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]       )






'''simple docstring'''






import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class      SCREAMING_SNAKE_CASE_	( snake_case					,					snake_case					,					unittest.TestCase		):
			__a					:      Dict					=  IFSuperResolutionPipeline
			__a					:      Dict					=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
			__a					:      Tuple					=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			__a					:      Tuple					=  PipelineTesterMixin.required_optional_params - {'''latents'''}




			def 			_snake_case   (      self       )			->						Dict:


								'''simple docstring'''






								return self._get_superresolution_dummy_components()




			def 			_snake_case   (      self					,      lowercase					,      lowercase=0       )			->						Union[str, Any]:


								'''simple docstring'''






								if str(lowercase       ).startswith('''mps'''       ):
													__SCREAMING_SNAKE_CASE					:							Optional[int]			   = torch.manual_seed(lowercase       )
								else:
													__SCREAMING_SNAKE_CASE					:							int			   = torch.Generator(device=lowercase       ).manual_seed(lowercase       )

								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = floats_tensor((1, 3, 3_2, 3_2)					,      rng=random.Random(lowercase       )       ).to(lowercase       )

								__SCREAMING_SNAKE_CASE					:							List[str]			   = {
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''image''': image,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''output_type''': '''numpy''',
								}

								return inputs




			@unittest.skipIf(
			    torch_device != '''cuda''' or not is_xformers_available()					,      reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,      )
			def 			_snake_case   (      self       )			->						List[str]:


								'''simple docstring'''






								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )




			def 			_snake_case   (      self       )			->						int:


								'''simple docstring'''






								self._test_save_load_optional_components()




			@unittest.skipIf(torch_device != '''cuda'''					,      reason='''float16 requires CUDA'''       )
			def 			_snake_case   (      self       )			->						Union[str, Any]:


								'''simple docstring'''






								super().test_save_load_floataa(expected_max_diff=1e-1       )




			def 			_snake_case   (      self       )			->						Optional[int]:


								'''simple docstring'''






								self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )




			def 			_snake_case   (      self       )			->						Optional[int]:


								'''simple docstring'''






								self._test_save_load_local()




			def 			_snake_case   (      self       )			->						List[str]:


								'''simple docstring'''






								self._test_inference_batch_single_identical(
								    expected_max_diff=1e-2					,      )


