
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 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       UpperCamelCase_				 =							(JukeboxModel,) if is_torch_available() else ()
       UpperCamelCase_				 =							'''openai/jukebox-1b-lyrics'''
       UpperCamelCase_				 =							{
           '''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
       UpperCamelCase_				 =							[
           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
       ]

       UpperCamelCase_				 =							[
           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
       ]

       UpperCamelCase_				 =							[
           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
       ]
       UpperCamelCase_				 =							[
           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
       ]

       UpperCamelCase_				 =							[
           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
       ]
       UpperCamelCase_				 =							[
           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
       ]

       UpperCamelCase_				 =							[1_058_304, 0, 786_432, 7_169, 507, 76, 27, 40, 30, 76]

       UpperCamelCase_				 =							[
           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
       ]
       UpperCamelCase_				 =							[
           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
       ]
       UpperCamelCase_				 =							[
           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
       ]
       UpperCamelCase_				 =							[
           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
       ]
       UpperCamelCase_				 =							[
           -0.0_4_9_2, -0.0_5_2_4, -0.0_5_6_5, -0.0_6_4_0, -0.0_6_8_6, -0.0_6_8_4, -0.0_6_7_7, -0.0_6_6_4,
           -0.0_6_0_5, -0.0_4_9_0, -0.0_3_3_0, -0.0_1_6_8, -0.0_0_8_3, -0.0_0_7_5, -0.0_0_5_1, 0.0_0_2_5,
           0.0_1_3_6, 0.0_2_6_1, 0.0_3_8_6, 0.0_4_9_7, 0.0_5_8_0, 0.0_5_9_9, 0.0_5_8_3, 0.0_6_1_4,
           0.0_7_4_0, 0.0_8_8_9, 0.1_0_2_3, 0.1_1_6_2, 0.1_2_1_1, 0.1_2_1_2, 0.1_2_5_1, 0.1_3_3_6,
           0.1_5_0_2, 0.1_6_8_6, 0.1_8_8_3, 0.2_1_4_8, 0.2_3_6_3, 0.2_4_5_8, 0.2_5_0_7, 0.2_5_3_1
       ]
       UpperCamelCase_				 =							[
           0.0_2_5_6, -0.0_5_4_4, 0.1_6_0_0, -0.0_0_3_2, 0.1_0_6_6, 0.0_8_2_5, -0.0_0_1_3, 0.3_4_4_0,
           0.0_2_1_0, 0.0_4_1_2, -0.1_7_7_7, -0.0_8_9_2, -0.0_1_6_4, 0.0_2_8_5, -0.0_6_1_3, -0.0_6_1_7,
           -0.0_1_3_7, -0.0_2_0_1, -0.0_1_7_5, 0.0_2_1_5, -0.0_6_2_7, 0.0_5_2_0, -0.0_7_3_0, 0.0_9_7_0,
           -0.0_1_0_0, 0.0_4_4_2, -0.0_5_8_6, 0.0_2_0_7, -0.0_0_1_5, -0.0_0_8_2
       ]
       UpperCamelCase_				 =							[
           0.0_4_1_5, 0.0_8_7_7, 0.0_0_2_2, -0.0_0_5_5, 0.0_7_5_1, 0.0_3_3_4, 0.0_3_2_4, -0.0_0_6_8,
           0.0_0_1_1, 0.0_0_1_7, -0.0_6_7_6, 0.0_6_5_5, -0.0_1_4_3, 0.0_3_9_9, 0.0_3_0_3, 0.0_7_4_3,
           -0.0_1_6_8, -0.0_3_9_4, -0.1_1_1_3, 0.0_1_2_4, 0.0_4_4_2, 0.0_2_6_7, -0.0_0_0_3, -0.1_5_3_6,
           -0.0_1_1_6, -0.1_8_3_7, -0.0_1_8_0, -0.1_0_2_6, -0.0_7_7_7, -0.0_4_5_6
       ]
       UpperCamelCase_				 =							[
           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 SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Optional[Any]:
         _lowerCAmelCase						=		JukeboxTokenizer.from_pretrained(self.model_id       )
         _lowerCAmelCase						=		tokenizer(**self.metas       )['input_ids']
         return tokens



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

         set_seed(0       )
         _lowerCAmelCase						=		[torch.zeros(1  ,      0  ,      dtype=torch.long       ).cpu() for _ in range(3       )]
         _lowerCAmelCase						=		model._sample(_lowerCAmelCase  ,      _lowerCAmelCase  ,      [0]  ,      sample_length=40 * model.priors[0].raw_to_tokens  ,      save_results=_lowerCAmelCase       )
         self.assertIn(zs[0][0].detach().cpu().tolist()  ,      [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]       )

         set_seed(0       )
         _lowerCAmelCase						=		model._sample(_lowerCAmelCase  ,      _lowerCAmelCase  ,      [1]  ,      sample_length=40 * model.priors[1].raw_to_tokens  ,      save_results=_lowerCAmelCase       )
         self.assertIn(zs[1][0].detach().cpu().tolist()  ,      [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]       )

         set_seed(0       )
         _lowerCAmelCase						=		model._sample(_lowerCAmelCase  ,      _lowerCAmelCase  ,      [2]  ,      sample_length=40 * model.priors[2].raw_to_tokens  ,      save_results=_lowerCAmelCase       )
         self.assertIn(zs[2][0].detach().cpu().tolist()  ,      [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]       )



       @slow
       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Dict:
         _lowerCAmelCase						=		False
         _lowerCAmelCase						=		JukeboxModel.from_pretrained(self.model_id  ,      min_duration=0       ).eval()

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

         _lowerCAmelCase						=		model.priors[0]
         _lowerCAmelCase						=		0
         _lowerCAmelCase						=		top_prior.get_music_tokens_conds(_lowerCAmelCase  ,      start=_lowerCAmelCase  ,      end=start + top_prior.n_ctx       )
         _lowerCAmelCase						=		top_prior.get_metadata(labels[0].clone()  ,      _lowerCAmelCase  ,      105_8304  ,      0       )

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

         _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		top_prior.get_cond(_lowerCAmelCase  ,      _lowerCAmelCase       )
         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 SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   Optional[int]:
         _lowerCAmelCase						=		False

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

         _lowerCAmelCase						=		[model.vqvae.encode(_lowerCAmelCase  ,      start_level=2  ,      bs_chunks=waveform.shape[0]       )[0], None, None]
         _lowerCAmelCase						=		model._sample(
             _lowerCAmelCase  ,      _lowerCAmelCase  ,      sample_levels=[0]  ,      save_results=_lowerCAmelCase  ,      sample_length=40 * model.priors[0].raw_to_tokens       )
         torch.testing.assert_allclose(zs[0][0][:40]  ,      torch.tensor(self.EXPECTED_PRIMED_0       )       )

         _lowerCAmelCase						=		torch.cat((zs[0], torch.zeros(1  ,      2048 - zs[0].shape[-1]       ))  ,      dim=-1       ).long()
         _lowerCAmelCase						=		[upper_a, model.vqvae.encode(_lowerCAmelCase  ,      start_level=1  ,      bs_chunks=waveform.shape[0]       )[0], None]
         _lowerCAmelCase						=		model._sample(
             _lowerCAmelCase  ,      _lowerCAmelCase  ,      sample_levels=[1]  ,      save_results=_lowerCAmelCase  ,      sample_length=40 * model.priors[1].raw_to_tokens       )
         torch.testing.assert_allclose(zs[1][0][:40]  ,      torch.tensor(self.EXPECTED_PRIMED_1       )       )

         _lowerCAmelCase						=		torch.cat((zs[1], torch.zeros(1  ,      2048 - zs[1].shape[-1]       ))  ,      dim=-1       ).long()
         _lowerCAmelCase						=		[upper_a, upper_a, model.vqvae.encode(_lowerCAmelCase  ,      start_level=0  ,      bs_chunks=waveform.shape[0]       )[0]]
         _lowerCAmelCase						=		model._sample(
             _lowerCAmelCase  ,      _lowerCAmelCase  ,      sample_levels=[2]  ,      save_results=_lowerCAmelCase  ,      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 SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Any:
         _lowerCAmelCase						=		JukeboxModel.from_pretrained(self.model_id  ,      min_duration=0       ).eval()
         set_seed(0       )
         _lowerCAmelCase						=		torch.rand((1, 5120, 1)       )
         with torch.no_grad():
           _lowerCAmelCase						=		model.vqvae.encode(_lowerCAmelCase  ,      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():
           _lowerCAmelCase						=		model.vqvae.decode(_lowerCAmelCase  ,      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 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       UpperCamelCase_				 =							(JukeboxModel,) if is_torch_available() else ()
       UpperCamelCase_				 =							'''openai/jukebox-5b-lyrics'''
       UpperCamelCase_				 =							{
           '''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
       UpperCamelCase_				 =							[
           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
       ]
       UpperCamelCase_				 =							[
           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
       ]

       UpperCamelCase_				 =							[
           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
       ]
       UpperCamelCase_				 =							[
           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
       ]

       UpperCamelCase_				 =							[
           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
       ]
       UpperCamelCase_				 =							[
           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
       ]

       UpperCamelCase_				 =							[
           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
       ]
       UpperCamelCase_				 =							[
           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
       ]

       UpperCamelCase_				 =							[
           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
       ]
       UpperCamelCase_				 =							[
           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 SCREAMING_SNAKE_CASE_				(     self					:			List[Any]  ,      _lowerCAmelCase					:			Optional[int]       )					->   Tuple:
         _lowerCAmelCase						=		JukeboxTokenizer.from_pretrained(_lowerCAmelCase       )
         _lowerCAmelCase						=		tokenizer(**self.metas       )['input_ids']
         return tokens



       @slow
       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   List[Any]:
         _lowerCAmelCase						=		JukeboxModel.from_pretrained(self.model_id  ,      min_duration=0       ).eval()
         _lowerCAmelCase						=		self.prepare_inputs(self.model_id       )

         set_seed(0       )
         _lowerCAmelCase						=		[torch.zeros(1  ,      0  ,      dtype=torch.long       ).cpu() for _ in range(3       )]
         _lowerCAmelCase						=		model._sample(_lowerCAmelCase  ,      _lowerCAmelCase  ,      [0]  ,      sample_length=60 * model.priors[0].raw_to_tokens  ,      save_results=_lowerCAmelCase       )
         self.assertIn(zs[0][0].detach().cpu().tolist()  ,      [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]       )

         set_seed(0       )
         _lowerCAmelCase						=		model._sample(_lowerCAmelCase  ,      _lowerCAmelCase  ,      [1]  ,      sample_length=60 * model.priors[1].raw_to_tokens  ,      save_results=_lowerCAmelCase       )
         self.assertIn(zs[1][0].detach().cpu().tolist()  ,      [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]       )

         set_seed(0       )
         _lowerCAmelCase						=		model._sample(_lowerCAmelCase  ,      _lowerCAmelCase  ,      [2]  ,      sample_length=60 * model.priors[2].raw_to_tokens  ,      save_results=_lowerCAmelCase       )
         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 SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   int:
         _lowerCAmelCase						=		JukeboxModel.from_pretrained(self.model_id  ,      min_duration=0       ).eval()
         _lowerCAmelCase						=		[i.cuda() for i in self.prepare_inputs(self.model_id       )]

         set_seed(0       )
         model.priors[0].cuda()
         _lowerCAmelCase						=		[torch.zeros(1  ,      0  ,      dtype=torch.long       ).cuda() for _ in range(3       )]
         _lowerCAmelCase						=		model._sample(_lowerCAmelCase  ,      _lowerCAmelCase  ,      [0]  ,      sample_length=60 * model.priors[0].raw_to_tokens  ,      save_results=_lowerCAmelCase       )
         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()
         _lowerCAmelCase						=		model._sample(_lowerCAmelCase  ,      _lowerCAmelCase  ,      [1]  ,      sample_length=60 * model.priors[1].raw_to_tokens  ,      save_results=_lowerCAmelCase       )
         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()
         _lowerCAmelCase						=		model._sample(_lowerCAmelCase  ,      _lowerCAmelCase  ,      [2]  ,      sample_length=60 * model.priors[2].raw_to_tokens  ,      save_results=_lowerCAmelCase       )
         torch.testing.assert_allclose(zs[2][0].cpu()  ,      torch.tensor(self.EXPECTED_GPU_OUTPUTS_0       )       )






       @slow
       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   List[Any]:
         _lowerCAmelCase						=		'ArthurZ/jukebox_prior_0'
         _lowerCAmelCase						=		JukeboxPrior.from_pretrained(_lowerCAmelCase  ,      min_duration=0       ).eval().half().to('cuda'       )

         _lowerCAmelCase						=		self.prepare_inputs(_lowerCAmelCase       )[0].cuda()
         _lowerCAmelCase						=		model.get_metadata(_lowerCAmelCase  ,      0  ,      7680  ,      0       )
         set_seed(0       )
         _lowerCAmelCase						=		model.sample(1  ,      metadata=_lowerCAmelCase  ,      sample_tokens=60       )
         self.assertIn(outputs[0].cpu().tolist()  ,      [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]       )


import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

_UpperCamelCase:					Tuple     =180



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[Any]		,				__SCREAMING_SNAKE_CASE					:  int    ):






  """simple docstring"""




  _lowerCAmelCase						=		k_size // 2
  _lowerCAmelCase	,       _lowerCAmelCase						=		np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  _lowerCAmelCase						=		(
      1
      / (2 * np.pi * sigma)
      * np.exp(-(np.square(__SCREAMING_SNAKE_CASE    ) + np.square(__SCREAMING_SNAKE_CASE    )) / (2 * np.square(__SCREAMING_SNAKE_CASE    ))    )
  )
  return g



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  Optional[Any]    ):






  """simple docstring"""




  _lowerCAmelCase						=		np.zeros(__SCREAMING_SNAKE_CASE    )

  for row in range(1		,				image_shape[0] - 1    ):
    for col in range(1		,				image_shape[1] - 1    ):
      _lowerCAmelCase						=		gradient_direction[row, col]

      if (
          0 <= direction < PI / 8
          or 15 * PI / 8 <= direction <= 2 * PI
          or 7 * PI / 8 <= direction <= 9 * PI / 8
      ):
        _lowerCAmelCase						=		sobel_grad[row, col - 1]
        _lowerCAmelCase						=		sobel_grad[row, col + 1]
        if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
          _lowerCAmelCase						=		sobel_grad[row, col]

      elif (
          PI / 8 <= direction < 3 * PI / 8
          or 9 * PI / 8 <= direction < 11 * PI / 8
      ):
        _lowerCAmelCase						=		sobel_grad[row + 1, col - 1]
        _lowerCAmelCase						=		sobel_grad[row - 1, col + 1]
        if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
          _lowerCAmelCase						=		sobel_grad[row, col]

      elif (
          3 * PI / 8 <= direction < 5 * PI / 8
          or 11 * PI / 8 <= direction < 13 * PI / 8
      ):
        _lowerCAmelCase						=		sobel_grad[row - 1, col]
        _lowerCAmelCase						=		sobel_grad[row + 1, col]
        if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
          _lowerCAmelCase						=		sobel_grad[row, col]

      elif (
          5 * PI / 8 <= direction < 7 * PI / 8
          or 13 * PI / 8 <= direction < 15 * PI / 8
      ):
        _lowerCAmelCase						=		sobel_grad[row - 1, col - 1]
        _lowerCAmelCase						=		sobel_grad[row + 1, col + 1]
        if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
          _lowerCAmelCase						=		sobel_grad[row, col]

  return destination



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]		,				__SCREAMING_SNAKE_CASE					:  Dict		,				__SCREAMING_SNAKE_CASE					:  List[Any]    ):






  """simple docstring"""




  for row in range(1		,				image_shape[0] - 1    ):
    for col in range(1		,				image_shape[1] - 1    ):
      if destination[row, col] >= threshold_high:
        _lowerCAmelCase						=		strong
      elif destination[row, col] <= threshold_low:
        _lowerCAmelCase						=		0
      else:
        _lowerCAmelCase						=		weak



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[Any]		,				__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]    ):






  """simple docstring"""




  for row in range(1		,				image_shape[0]    ):
    for col in range(1		,				image_shape[1]    ):
      if destination[row, col] == weak:
        if 255 in (
            destination[row, col + 1],
            destination[row, col - 1],
            destination[row - 1, col],
            destination[row + 1, col],
            destination[row - 1, col - 1],
            destination[row + 1, col - 1],
            destination[row - 1, col + 1],
            destination[row + 1, col + 1],
        ):
          _lowerCAmelCase						=		strong
        else:
          _lowerCAmelCase						=		0



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  int=15		,				__SCREAMING_SNAKE_CASE					:  int=30		,				__SCREAMING_SNAKE_CASE					:  Tuple=128		,				__SCREAMING_SNAKE_CASE					:  List[Any]=255    ):






  """simple docstring"""




  _lowerCAmelCase						=		img_convolve(__SCREAMING_SNAKE_CASE		,				gen_gaussian_kernel(9		,				sigma=1.4    )    )
  # get the gradient and degree by sobel_filter
  _lowerCAmelCase	,       _lowerCAmelCase						=		sobel_filter(__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		PI + np.radadeg(__SCREAMING_SNAKE_CASE    )

  _lowerCAmelCase						=		suppress_non_maximum(image.shape		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  detect_high_low_threshold(
      image.shape		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  track_edge(image.shape		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  return destination


if __name__ == "__main__":
       # read original image in gray mode
       _UpperCamelCase:					Optional[Any]     =cva.imread(R'../image_data/lena.jpg', 0)
       # canny edge detection
       _UpperCamelCase:					Tuple     =canny(lena)
       cva.imshow('canny', canny_destination)
       cva.waitKey(0)
