





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      :							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]					)













import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ConvNextImageProcessor

class 				A    (unittest.TestCase				):



      def __init__(					self     ,  lowercase_     ,  lowercase_=7     ,  lowercase_=3     ,  lowercase_=18     ,  lowercase_=30     ,  lowercase_=400     ,  lowercase_=True     ,  lowercase_=None     ,  lowercase_=0.875     ,  lowercase_=True     ,  lowercase_=[0.5, 0.5, 0.5]     ,  lowercase_=[0.5, 0.5, 0.5]     ,  )      ->   List[str]:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						size if size is not None else {'''shortest_edge''': 20}
             _snake_case      :							List[Any]     				=						parent
             _snake_case      :							Optional[int]     				=						batch_size
             _snake_case      :							str     				=						num_channels
             _snake_case      :							Union[str, Any]     				=						image_size
             _snake_case      :							Optional[Any]     				=						min_resolution
             _snake_case      :							Union[str, Any]     				=						max_resolution
             _snake_case      :							Tuple     				=						do_resize
             _snake_case      :							List[Any]     				=						size
             _snake_case      :							Optional[int]     				=						crop_pct
             _snake_case      :							Any     				=						do_normalize
             _snake_case      :							Optional[int]     				=						image_mean
             _snake_case      :							Union[str, Any]     				=						image_std



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             return {
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_normalize": self.do_normalize,
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "crop_pct": self.crop_pct,
             }


@require_torch
@require_vision
class 				A    (__UpperCAmelCase       ,unittest.TestCase				):
      _SCREAMING_SNAKE_CASE					=							ConvNextImageProcessor if is_vision_available() else None



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						ConvNextImageProcessingTester(self					)



      @property
      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             return self.image_processor_tester.prepare_image_processor_dict()



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						self.image_processing_class(**self.image_processor_dict					)
             self.assertTrue(hasattr(lowercase_     ,  '''do_resize'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''size'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''crop_pct'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''do_normalize'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''image_mean'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''image_std'''					)					)



      def    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             _snake_case      :							int     				=						self.image_processing_class.from_dict(self.image_processor_dict					)
             self.assertEqual(image_processor.size     ,  {'''shortest_edge''': 20}					)

             _snake_case      :							Tuple     				=						self.image_processing_class.from_dict(self.image_processor_dict     ,  size=42					)
             self.assertEqual(image_processor.size     ,  {'''shortest_edge''': 42}					)



      def    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             pass



      def    __a (					self					)      ->   int:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random PIL images
             _snake_case      :							List[str]     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  Image.Image					)

             # Test not batched input
             _snake_case      :							List[str]     				=						image_processing(image_inputs[0]     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )

             # Test batched
             _snake_case      :							int     				=						image_processing(lowercase_     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random numpy tensors
             _snake_case      :							Tuple     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  numpify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  np.ndarray					)

             # Test not batched input
             _snake_case      :							List[str]     				=						image_processing(image_inputs[0]     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )

             # Test batched
             _snake_case      :							Union[str, Any]     				=						image_processing(lowercase_     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )



      def    __a (					self					)      ->   Union[str, Any]:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random PyTorch tensors
             _snake_case      :							Dict     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  torchify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  torch.Tensor					)

             # Test not batched input
             _snake_case      :							Union[str, Any]     				=						image_processing(image_inputs[0]     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )

             # Test batched
             _snake_case      :							List[Any]     				=						image_processing(lowercase_     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )






