code: 
  
  
  
  
  
  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				):
        _SCREAMING_SNAKE_CASE					=							(JukeboxModel,) if is_torch_available() else ()
        _SCREAMING_SNAKE_CASE					=							"""openai/jukebox-1b-lyrics"""
        _SCREAMING_SNAKE_CASE					=							{
            """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
        _SCREAMING_SNAKE_CASE					=							[
            1_864, 1_536, 1_213, 1_870, 1_357, 1_536, 519, 880, 1_323, 789, 1_082, 534,
            1_000, 1_445, 1_105, 1_130, 967, 515, 1_434, 1_620, 534, 1_495, 283, 1_445,
            333, 1_307, 539, 1_631, 1_528, 375, 1_434, 673, 627, 710, 778, 1_883,
            1_405, 1_276, 1_455, 1_228
        ]
  
        _SCREAMING_SNAKE_CASE					=							[
            1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
            653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
            653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
            653, 653, 653, 653
        ]
  
        _SCREAMING_SNAKE_CASE					=							[
            1_125, 1_751, 697, 1_776, 1_141, 1_476, 391, 697, 1_125, 684, 867, 416,
            844, 1_372, 1_274, 717, 1_274, 844, 1_299, 1_419, 697, 1_370, 317, 1_125,
            191, 1_440, 1_370, 1_440, 1_370, 282, 1_621, 1_370, 368, 349, 867, 1_872,
            1_262, 869, 1_728, 747
        ]
        _SCREAMING_SNAKE_CASE					=							[
            416, 416, 1_125, 1_125, 416, 416, 416, 416, 416, 416, 416, 416,
            416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
            416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
            416, 416, 416, 416
        ]
  
        _SCREAMING_SNAKE_CASE					=							[
            1_755, 842, 307, 1_843, 1_022, 1_395, 234, 1_554, 806, 739, 1_022, 442,
            616, 556, 268, 1_499, 933, 457, 1_440, 1_837, 755, 985, 308, 902,
            293, 1_443, 1_671, 1_141, 1_533, 555, 1_562, 1_061, 287, 417, 1_022, 2_008,
            1_186, 1_015, 1_777, 268
        ]
        _SCREAMING_SNAKE_CASE					=							[
            854, 842, 1_353, 114, 1_353, 842, 185, 842, 185, 114, 591, 842,
            185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842,
            591, 842, 1_353, 842, 185, 842, 591, 842, 591, 114, 591, 842,
            185, 842, 591, 89
        ]
  
        _SCREAMING_SNAKE_CASE					=							[1_058_304, 0, 786_432, 7_169, 507, 76, 27, 40, 30, 76]
  
        _SCREAMING_SNAKE_CASE					=							[
            390, 1_160, 1_002, 1_907, 1_788, 1_788, 1_788, 1_907, 1_002, 1_002, 1_854, 1_002,
            1_002, 1_002, 1_002, 1_002, 1_002, 1_160, 1_160, 1_606, 596, 596, 1_160, 1_002,
            1_516, 596, 1_002, 1_002, 1_002, 1_907, 1_788, 1_788, 1_788, 1_854, 1_788, 1_907,
            1_907, 1_788, 596, 1_626
        ]
        _SCREAMING_SNAKE_CASE					=							[
            1_236, 1_668, 1_484, 1_920, 1_848, 1_409, 139, 864, 1_828, 1_272, 1_599, 824,
            1_672, 139, 555, 1_484, 824, 1_920, 555, 596, 1_579, 1_599, 1_231, 1_599,
            1_637, 1_407, 212, 824, 1_599, 116, 1_433, 824, 258, 1_599, 1_433, 1_895,
            1_063, 1_433, 1_433, 1_599
        ]
        _SCREAMING_SNAKE_CASE					=							[
            1_684, 1_873, 1_119, 1_189, 395, 611, 1_901, 972, 890, 1_337, 1_392, 1_927,
            96, 972, 672, 780, 1_119, 890, 158, 771, 1_073, 1_927, 353, 1_331,
            1_269, 1_459, 1_333, 1_645, 812, 1_577, 1_337, 606, 353, 981, 1_466, 619,
            197, 391, 302, 1_930
        ]
        _SCREAMING_SNAKE_CASE					=							[
            390, 1_160, 1_002, 1_907, 1_788, 1_788, 1_788, 1_907, 1_002, 1_002, 1_854, 1_002,
            1_002, 1_002, 1_002, 1_002, 1_002, 1_160, 1_160, 1_606, 596, 596, 1_160, 1_002,
            1_516, 596, 1_002, 1_002, 1_002, 1_907, 1_788, 1_788, 1_788, 1_854, 1_788, 1_907,
            1_907, 1_788, 596, 1_626
        ]
        _SCREAMING_SNAKE_CASE					=							[
            -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
        ]
        _SCREAMING_SNAKE_CASE					=							[
            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
        ]
        _SCREAMING_SNAKE_CASE					=							[
            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
        ]
        _SCREAMING_SNAKE_CASE					=							[
            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					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Dict     				=						JukeboxTokenizer.from_pretrained(self.model_id					)
               _snake_case      :							Optional[int]     				=						tokenizer(**self.metas					)['''input_ids''']
               return tokens
  
  
  
        @slow
        def    __a (					self					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						JukeboxModel.from_pretrained(self.model_id     ,  min_duration=0					).eval()
               _snake_case      :							List[Any]     				=						self.prepare_inputs()
  
               set_seed(0					)
               _snake_case      :							str     				=						[torch.zeros(1     ,  0     ,  dtype=torch.long					).cpu() for _ in range(3					)]
               _snake_case      :							List[Any]     				=						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					)
               _snake_case      :							Any     				=						model._sample(lowercase_     ,  lowercase_     ,  [1]     ,  sample_length=40 * model.priors[1].raw_to_tokens     ,  save_results=lowercase_					)
               self.assertIn(zs[1][0].detach().cpu().tolist()     ,  [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]					)
  
               set_seed(0					)
               _snake_case      :							Tuple     				=						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    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						False
               _snake_case      :							int     				=						JukeboxModel.from_pretrained(self.model_id     ,  min_duration=0					).eval()
  
               _snake_case      :							Tuple     				=						self.prepare_inputs()
               set_seed(0					)
               _snake_case      :							Optional[Any]     				=						[torch.zeros(1     ,  0     ,  dtype=torch.long					) for _ in range(3					)]
  
               _snake_case      :							List[str]     				=						model.priors[0]
               _snake_case      :							Optional[int]     				=						0
               _snake_case      :							Any     				=						top_prior.get_music_tokens_conds(lowercase_     ,  start=lowercase_     ,  end=start + top_prior.n_ctx					)
               _snake_case      :							str     				=						top_prior.get_metadata(labels[0].clone()     ,  lowercase_     ,  105_8304     ,  0					)
  
               self.assertIsNone(lowercase_					)
               self.assertListEqual(metadata.numpy()[0][:10].tolist()     ,  self.EXPECTED_Y_COND					)
  
               _snake_case				,    _snake_case				,    _snake_case      :							Dict     				=						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    __a (					self					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							str     				=						False
  
               _snake_case      :							Optional[Any]     				=						JukeboxModel.from_pretrained(self.model_id     ,  min_duration=0					).eval()
               set_seed(0					)
               _snake_case      :							str     				=						torch.rand((1, 5120, 1)					)
               _snake_case      :							Union[str, Any]     				=						list(self.prepare_inputs()					)
  
               _snake_case      :							int     				=						[model.vqvae.encode(lowercase_     ,  start_level=2     ,  bs_chunks=waveform.shape[0]					)[0], None, None]
               _snake_case      :							Optional[Any]     				=						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					)					)
  
               _snake_case      :							List[Any]     				=						torch.cat((zs[0], torch.zeros(1     ,  2048 - zs[0].shape[-1]					))     ,  dim=-1					).long()
               _snake_case      :							Dict     				=						[upper_a, model.vqvae.encode(lowercase_     ,  start_level=1     ,  bs_chunks=waveform.shape[0]					)[0], None]
               _snake_case      :							Optional[int]     				=						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					)					)
  
               _snake_case      :							Any     				=						torch.cat((zs[1], torch.zeros(1     ,  2048 - zs[1].shape[-1]					))     ,  dim=-1					).long()
               _snake_case      :							Optional[int]     				=						[upper_a, upper_a, model.vqvae.encode(lowercase_     ,  start_level=0     ,  bs_chunks=waveform.shape[0]					)[0]]
               _snake_case      :							List[str]     				=						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    __a (					self					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							str     				=						JukeboxModel.from_pretrained(self.model_id     ,  min_duration=0					).eval()
               set_seed(0					)
               _snake_case      :							str     				=						torch.rand((1, 5120, 1)					)
               with torch.no_grad():
                      _snake_case      :							Dict     				=						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():
                      _snake_case      :							Dict     				=						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				):
        _SCREAMING_SNAKE_CASE					=							(JukeboxModel,) if is_torch_available() else ()
        _SCREAMING_SNAKE_CASE					=							"""openai/jukebox-5b-lyrics"""
        _SCREAMING_SNAKE_CASE					=							{
            """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
        _SCREAMING_SNAKE_CASE					=							[
            1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
            653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
            653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
            653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
            1_489, 1_489, 1_489, 1_489, 1_150, 1_853, 1_509, 1_150, 1_357, 1_509, 6, 1_272
        ]
        _SCREAMING_SNAKE_CASE					=							[
            1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
            653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
            653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
            653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
            653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
        ]
  
        _SCREAMING_SNAKE_CASE					=							[
            1_125, 416, 1_125, 1_125, 1_125, 1_125, 1_125, 416, 416, 416, 416, 416,
            416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
            416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
            416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
            416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
        ]
        _SCREAMING_SNAKE_CASE					=							[
            416, 416, 1_125, 1_125, 416, 416, 416, 416, 416, 416, 416, 416, 416,
            416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
            416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
            416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
            416, 416, 416, 416, 416, 416, 416, 416
        ]
  
        _SCREAMING_SNAKE_CASE					=							[
            1_755, 1_061, 234, 1_755, 1_061, 1_755, 185, 290, 307, 307, 616, 616,
            616, 616, 616, 616, 307, 290, 417, 1_755, 234, 1_755, 185, 290,
            290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1_755,
            234, 234, 1_755, 234, 185, 185, 307, 616, 616, 616, 616, 290,
            1_755, 1_755, 1_755, 234, 234, 1_755, 1_572, 290, 307, 616, 34, 616
        ]
        _SCREAMING_SNAKE_CASE					=							[
            854, 842, 1_353, 114, 1_353, 842, 185, 842, 185, 114, 591, 842, 185,
            417, 185, 842, 307, 842, 591, 842, 185, 842, 185, 842, 591, 842,
            1_353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591,
            89, 591, 842, 591, 842, 591, 417, 1_372, 842, 1_372, 842, 34, 842,
            185, 89, 591, 842, 185, 842, 591, 632
        ]
  
        _SCREAMING_SNAKE_CASE					=							[
            1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
            653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
            653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
            653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
            653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
        ]
        _SCREAMING_SNAKE_CASE					=							[
            1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
            653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
            653, 653, 653, 653, 653, 653, 653, 1_853, 1_177, 1_536, 1_228,
            710, 475, 1_489, 1_229, 1_224, 231, 1_224, 252, 1_434, 653, 475,
            1_106, 1_877, 1_599, 1_228, 1_600, 1_683, 1_182, 1_853, 475, 1_864,
            252, 1_229, 1_434, 2_001
        ]
  
        _SCREAMING_SNAKE_CASE					=							[
            1_125, 1_125, 416, 1_125, 1_125, 416, 1_125, 1_125, 416, 416, 1_125, 416,
            416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
            416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
            416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
            416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
        ]
        _SCREAMING_SNAKE_CASE					=							[
            491, 1_755, 34, 1_613, 1_755, 417, 992, 1_613, 222, 842, 1_353, 1_613,
            844, 632, 185, 1_613, 844, 632, 185, 1_613, 185, 842, 677, 1_613,
            185, 114, 1_353, 1_613, 307, 89, 844, 1_613, 307, 1_332, 234, 1_979,
            307, 89, 1_353, 616, 34, 842, 185, 842, 34, 842, 185, 842,
            307, 114, 185, 89, 34, 1_268, 185, 89, 34, 842, 185, 89
        ]
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						JukeboxTokenizer.from_pretrained(lowercase_					)
               _snake_case      :							Union[str, Any]     				=						tokenizer(**self.metas					)['''input_ids''']
               return tokens
  
  
  
        @slow
        def    __a (					self					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						JukeboxModel.from_pretrained(self.model_id     ,  min_duration=0					).eval()
               _snake_case      :							List[str]     				=						self.prepare_inputs(self.model_id					)
  
               set_seed(0					)
               _snake_case      :							Optional[Any]     				=						[torch.zeros(1     ,  0     ,  dtype=torch.long					).cpu() for _ in range(3					)]
               _snake_case      :							str     				=						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					)
               _snake_case      :							Dict     				=						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					)
               _snake_case      :							int     				=						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    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						JukeboxModel.from_pretrained(self.model_id     ,  min_duration=0					).eval()
               _snake_case      :							Union[str, Any]     				=						[i.cuda() for i in self.prepare_inputs(self.model_id					)]
  
               set_seed(0					)
               model.priors[0].cuda()
               _snake_case      :							List[Any]     				=						[torch.zeros(1     ,  0     ,  dtype=torch.long					).cuda() for _ in range(3					)]
               _snake_case      :							str     				=						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()
               _snake_case      :							Tuple     				=						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()
               _snake_case      :							List[str]     				=						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    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						'''ArthurZ/jukebox_prior_0'''
               _snake_case      :							str     				=						JukeboxPrior.from_pretrained(lowercase_     ,  min_duration=0					).eval().half().to('''cuda'''					)
  
               _snake_case      :							Union[str, Any]     				=						self.prepare_inputs(lowercase_					)[0].cuda()
               _snake_case      :							int     				=						model.get_metadata(lowercase_     ,  0     ,  7680     ,  0					)
               set_seed(0					)
               _snake_case      :							List[str]     				=						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]					)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import unittest
  
  from transformers import load_tool
  from transformers.tools.agent_types import AGENT_TYPE_MAPPING
  
  from .test_tools_common import ToolTesterMixin, output_types
  
  class 				A    (unittest.TestCase       ,__UpperCAmelCase				):
  
  
  
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						load_tool('''translation'''					)
               self.tool.setup()
               _snake_case      :							Any     				=						load_tool('''translation'''     ,  remote=lowercase_					)
  
  
  
        def    __a (					self					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						self.tool('''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
               self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)
  
  
  
        def    __a (					self					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						self.remote_tool('''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
               self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)
  
  
  
        def    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Dict     				=						self.tool(text='''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
               self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)
  
  
  
        def    __a (					self					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						self.remote_tool(text='''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
               self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)
  
  
  
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
               _snake_case      :							Dict     				=						self.tool(*lowercase_					)
  
               # There is a single output
               if len(self.tool.outputs					) == 1:
                      _snake_case      :							Optional[Any]     				=						[outputs]
  
               self.assertListEqual(output_types(lowercase_					)     ,  self.tool.outputs					)
  
  
  
        def    __a (					self					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Union[str, Any]     				=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
               _snake_case      :							Dict     				=						self.tool(*lowercase_					)
  
               if not isinstance(lowercase_     ,  lowercase_					):
                      _snake_case      :							Optional[Any]     				=						[outputs]
  
               self.assertEqual(len(lowercase_					)     ,  len(self.tool.outputs					)					)
  
               for output, output_type in zip(lowercase_     ,  self.tool.outputs					):
                      _snake_case      :							List[str]     				=						AGENT_TYPE_MAPPING[output_type]
                      self.assertTrue(isinstance(lowercase_     ,  lowercase_					)					)
  
  
  
        def    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Dict     				=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
  
               _snake_case      :							Any     				=						[]
  
               for _input, input_type in zip(lowercase_     ,  self.tool.inputs					):
                      if isinstance(lowercase_     ,  lowercase_					):
                             _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input					) for _input_type in input_type]					)
                      else:
                             _inputs.append(AGENT_TYPE_MAPPING[input_type](_input					)					)
  
          # Should not raise an error
               _snake_case      :							List[Any]     				=						self.tool(*lowercase_					)
  
               if not isinstance(lowercase_     ,  lowercase_					):
                      _snake_case      :							Any     				=						[outputs]
  
               self.assertEqual(len(lowercase_					)     ,  len(self.tool.outputs					)					)
  
  
  
  
  
  
  