
'''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 			UpperCamelCase__	(     unittest.TestCase):
 UpperCAmelCase__     :     Optional[int]    					=       (JukeboxModel,) if is_torch_available() else ()
 UpperCAmelCase__     :     str    					=       'openai/jukebox-1b-lyrics'
 UpperCAmelCase__     :     Any    					=       {
     'artist': 'Zac Brown Band',
     'genres': 'Country',
     'lyrics': 'I met a traveller from an antique land,\n    Who said "Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ',
 }
 # fmt: off
 UpperCAmelCase__     :     List[str]    					=       [
     1_8_6_4, 1_5_3_6, 1_2_1_3, 1_8_7_0, 1_3_5_7, 1_5_3_6, 5_1_9, 8_8_0, 1_3_2_3, 7_8_9, 1_0_8_2, 5_3_4,
     1_0_0_0, 1_4_4_5, 1_1_0_5, 1_1_3_0, 9_6_7, 5_1_5, 1_4_3_4, 1_6_2_0, 5_3_4, 1_4_9_5, 2_8_3, 1_4_4_5,
     3_3_3, 1_3_0_7, 5_3_9, 1_6_3_1, 1_5_2_8, 3_7_5, 1_4_3_4, 6_7_3, 6_2_7, 7_1_0, 7_7_8, 1_8_8_3,
     1_4_0_5, 1_2_7_6, 1_4_5_5, 1_2_2_8
 ]

 UpperCAmelCase__     :     int    					=       [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3
 ]

 UpperCAmelCase__     :     Dict    					=       [
     1_1_2_5, 1_7_5_1, 6_9_7, 1_7_7_6, 1_1_4_1, 1_4_7_6, 3_9_1, 6_9_7, 1_1_2_5, 6_8_4, 8_6_7, 4_1_6,
     8_4_4, 1_3_7_2, 1_2_7_4, 7_1_7, 1_2_7_4, 8_4_4, 1_2_9_9, 1_4_1_9, 6_9_7, 1_3_7_0, 3_1_7, 1_1_2_5,
     1_9_1, 1_4_4_0, 1_3_7_0, 1_4_4_0, 1_3_7_0, 2_8_2, 1_6_2_1, 1_3_7_0, 3_6_8, 3_4_9, 8_6_7, 1_8_7_2,
     1_2_6_2, 8_6_9, 1_7_2_8, 7_4_7
 ]
 UpperCAmelCase__     :     List[Any]    					=       [
     4_1_6, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6
 ]

 UpperCAmelCase__     :     str    					=       [
     1_7_5_5, 8_4_2, 3_0_7, 1_8_4_3, 1_0_2_2, 1_3_9_5, 2_3_4, 1_5_5_4, 8_0_6, 7_3_9, 1_0_2_2, 4_4_2,
     6_1_6, 5_5_6, 2_6_8, 1_4_9_9, 9_3_3, 4_5_7, 1_4_4_0, 1_8_3_7, 7_5_5, 9_8_5, 3_0_8, 9_0_2,
     2_9_3, 1_4_4_3, 1_6_7_1, 1_1_4_1, 1_5_3_3, 5_5_5, 1_5_6_2, 1_0_6_1, 2_8_7, 4_1_7, 1_0_2_2, 2_0_0_8,
     1_1_8_6, 1_0_1_5, 1_7_7_7, 2_6_8
 ]
 UpperCAmelCase__     :     Optional[Any]    					=       [
     8_5_4, 8_4_2, 1_3_5_3, 1_1_4, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 1_1_4, 5_9_1, 8_4_2,
     1_8_5, 4_1_7, 1_8_5, 8_4_2, 3_0_7, 8_4_2, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 3_0_7, 8_4_2,
     5_9_1, 8_4_2, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 1_1_4, 5_9_1, 8_4_2,
     1_8_5, 8_4_2, 5_9_1, 8_9
 ]

 UpperCAmelCase__     :     Any    					=       [1_0_5_8_3_0_4, 0, 7_8_6_4_3_2, 7_1_6_9, 5_0_7, 7_6, 2_7, 4_0, 3_0, 7_6]

 UpperCAmelCase__     :     int    					=       [
     3_9_0, 1_1_6_0, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_9_0_7, 1_0_0_2, 1_0_0_2, 1_8_5_4, 1_0_0_2,
     1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_1_6_0, 1_1_6_0, 1_6_0_6, 5_9_6, 5_9_6, 1_1_6_0, 1_0_0_2,
     1_5_1_6, 5_9_6, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_8_5_4, 1_7_8_8, 1_9_0_7,
     1_9_0_7, 1_7_8_8, 5_9_6, 1_6_2_6
 ]
 UpperCAmelCase__     :     Optional[int]    					=       [
     1_2_3_6, 1_6_6_8, 1_4_8_4, 1_9_2_0, 1_8_4_8, 1_4_0_9, 1_3_9, 8_6_4, 1_8_2_8, 1_2_7_2, 1_5_9_9, 8_2_4,
     1_6_7_2, 1_3_9, 5_5_5, 1_4_8_4, 8_2_4, 1_9_2_0, 5_5_5, 5_9_6, 1_5_7_9, 1_5_9_9, 1_2_3_1, 1_5_9_9,
     1_6_3_7, 1_4_0_7, 2_1_2, 8_2_4, 1_5_9_9, 1_1_6, 1_4_3_3, 8_2_4, 2_5_8, 1_5_9_9, 1_4_3_3, 1_8_9_5,
     1_0_6_3, 1_4_3_3, 1_4_3_3, 1_5_9_9
 ]
 UpperCAmelCase__     :     int    					=       [
     1_6_8_4, 1_8_7_3, 1_1_1_9, 1_1_8_9, 3_9_5, 6_1_1, 1_9_0_1, 9_7_2, 8_9_0, 1_3_3_7, 1_3_9_2, 1_9_2_7,
     9_6, 9_7_2, 6_7_2, 7_8_0, 1_1_1_9, 8_9_0, 1_5_8, 7_7_1, 1_0_7_3, 1_9_2_7, 3_5_3, 1_3_3_1,
     1_2_6_9, 1_4_5_9, 1_3_3_3, 1_6_4_5, 8_1_2, 1_5_7_7, 1_3_3_7, 6_0_6, 3_5_3, 9_8_1, 1_4_6_6, 6_1_9,
     1_9_7, 3_9_1, 3_0_2, 1_9_3_0
 ]
 UpperCAmelCase__     :     Optional[int]    					=       [
     3_9_0, 1_1_6_0, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_9_0_7, 1_0_0_2, 1_0_0_2, 1_8_5_4, 1_0_0_2,
     1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_1_6_0, 1_1_6_0, 1_6_0_6, 5_9_6, 5_9_6, 1_1_6_0, 1_0_0_2,
     1_5_1_6, 5_9_6, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_8_5_4, 1_7_8_8, 1_9_0_7,
     1_9_0_7, 1_7_8_8, 5_9_6, 1_6_2_6
 ]
 UpperCAmelCase__     :     Any    					=       [
     -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__     :     Dict    					=       [
     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__     :     List[str]    					=       [
     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__     :     Optional[Any]    					=       [
     7_6, 2_7, 4_0, 3_0, 7_6, 4_6, 4_4, 4_7, 4_0, 3_7, 3_8, 3_1, 4_5, 4_5, 7_6, 3_8, 3_1, 3_3,
     4_5, 7_6, 4_1, 3_2, 7_6, 4_5, 4_6, 4_1, 4_0, 3_1, 7_8, 7_6
 ]






 def 						lowercase_    (      self	:Tuple      )					->      List[Any]:







       '''simple docstring'''



       __A													=      JukeboxTokenizer.from_pretrained(self.model_id      )
       __A													=      tokenizer(**self.metas      )['input_ids']
       return tokens






 @slow
 def 						lowercase_    (      self	:Tuple      )					->      int:







       '''simple docstring'''



       __A													=      JukeboxModel.from_pretrained(self.model_id						,					min_duration=0      ).eval()
       __A													=      self.prepare_inputs()

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

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

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






 @slow
 def 						lowercase_    (      self	:Any      )					->      str:







       '''simple docstring'''



       __A													=      False
       __A													=      JukeboxModel.from_pretrained(self.model_id						,					min_duration=0      ).eval()

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

       __A													=      model.priors[0]
       __A													=      0
       __A													=      top_prior.get_music_tokens_conds(_A						,					start=_A						,					end=start + top_prior.n_ctx      )
       __A													=      top_prior.get_metadata(labels[0].clone()						,					_A						,					1_058_304						,					0      )

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

       __A						,   __A						,   __A													=      top_prior.get_cond(_A						,					_A      )
       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 						lowercase_    (      self	:Dict      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      False

       __A													=      JukeboxModel.from_pretrained(self.model_id						,					min_duration=0      ).eval()
       set_seed(0      )
       __A													=      torch.rand((1, 5_120, 1)      )
       __A													=      list(self.prepare_inputs()      )

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

       __A													=      torch.cat((zs[0], torch.zeros(1						,					2_048 - zs[0].shape[-1]      ))						,					dim=-1      ).long()
       __A													=      [upper_a, model.vqvae.encode(_A						,					start_level=1						,					bs_chunks=waveform.shape[0]      )[0], None]
       __A													=      model._sample(
           _A						,					_A						,					sample_levels=[1]						,					save_results=_A						,					sample_length=40 * model.priors[1].raw_to_tokens      )
       torch.testing.assert_allclose(zs[1][0][:40]						,					torch.tensor(self.EXPECTED_PRIMED_1      )      )

       __A													=      torch.cat((zs[1], torch.zeros(1						,					2_048 - zs[1].shape[-1]      ))						,					dim=-1      ).long()
       __A													=      [upper_a, upper_a, model.vqvae.encode(_A						,					start_level=0						,					bs_chunks=waveform.shape[0]      )[0]]
       __A													=      model._sample(
           _A						,					_A						,					sample_levels=[2]						,					save_results=_A						,					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 						lowercase_    (      self	:Union[str, Any]      )					->      str:







       '''simple docstring'''



       __A													=      JukeboxModel.from_pretrained(self.model_id						,					min_duration=0      ).eval()
       set_seed(0      )
       __A													=      torch.rand((1, 5_120, 1)      )
       with torch.no_grad():
             __A													=      model.vqvae.encode(_A						,					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():
             __A													=      model.vqvae.decode(_A						,					start_level=2						,					bs_chunks=x.shape[0]      )
       torch.testing.assert_allclose(x[0, :40, 0]						,					torch.tensor(self.EXPECTED_VQVAE_DECODE      )						,					atol=1E-4						,					rtol=1E-4      )


@require_torch
class 			UpperCamelCase__	(     unittest.TestCase):
 UpperCAmelCase__     :     int    					=       (JukeboxModel,) if is_torch_available() else ()
 UpperCAmelCase__     :     Optional[int]    					=       'openai/jukebox-5b-lyrics'
 UpperCAmelCase__     :     Any    					=       {
     'artist': 'Zac Brown Band',
     'genres': 'Country',
     'lyrics': 'I met a traveller from an antique land,\n    Who said "Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ',
 }

 # fmt: off
 UpperCAmelCase__     :     Union[str, Any]    					=       [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     1_4_8_9, 1_4_8_9, 1_4_8_9, 1_4_8_9, 1_1_5_0, 1_8_5_3, 1_5_0_9, 1_1_5_0, 1_3_5_7, 1_5_0_9, 6, 1_2_7_2
 ]
 UpperCAmelCase__     :     Dict    					=       [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3
 ]

 UpperCAmelCase__     :     Dict    					=       [
     1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 1_1_2_5, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
 ]
 UpperCAmelCase__     :     Optional[int]    					=       [
     4_1_6, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
 ]

 UpperCAmelCase__     :     List[str]    					=       [
     1_7_5_5, 1_0_6_1, 2_3_4, 1_7_5_5, 1_0_6_1, 1_7_5_5, 1_8_5, 2_9_0, 3_0_7, 3_0_7, 6_1_6, 6_1_6,
     6_1_6, 6_1_6, 6_1_6, 6_1_6, 3_0_7, 2_9_0, 4_1_7, 1_7_5_5, 2_3_4, 1_7_5_5, 1_8_5, 2_9_0,
     2_9_0, 2_9_0, 3_0_7, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 2_9_0, 2_3_4, 2_3_4, 1_7_5_5,
     2_3_4, 2_3_4, 1_7_5_5, 2_3_4, 1_8_5, 1_8_5, 3_0_7, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 2_9_0,
     1_7_5_5, 1_7_5_5, 1_7_5_5, 2_3_4, 2_3_4, 1_7_5_5, 1_5_7_2, 2_9_0, 3_0_7, 6_1_6, 3_4, 6_1_6
 ]
 UpperCAmelCase__     :     Tuple    					=       [
     8_5_4, 8_4_2, 1_3_5_3, 1_1_4, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 1_1_4, 5_9_1, 8_4_2, 1_8_5,
     4_1_7, 1_8_5, 8_4_2, 3_0_7, 8_4_2, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2,
     1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 1_1_4, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 5_9_1,
     8_9, 5_9_1, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 4_1_7, 1_3_7_2, 8_4_2, 1_3_7_2, 8_4_2, 3_4, 8_4_2,
     1_8_5, 8_9, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 6_3_2
 ]

 UpperCAmelCase__     :     List[Any]    					=       [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3
 ]
 UpperCAmelCase__     :     Optional[Any]    					=       [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 1_8_5_3, 1_1_7_7, 1_5_3_6, 1_2_2_8,
     7_1_0, 4_7_5, 1_4_8_9, 1_2_2_9, 1_2_2_4, 2_3_1, 1_2_2_4, 2_5_2, 1_4_3_4, 6_5_3, 4_7_5,
     1_1_0_6, 1_8_7_7, 1_5_9_9, 1_2_2_8, 1_6_0_0, 1_6_8_3, 1_1_8_2, 1_8_5_3, 4_7_5, 1_8_6_4,
     2_5_2, 1_2_2_9, 1_4_3_4, 2_0_0_1
 ]

 UpperCAmelCase__     :     List[str]    					=       [
     1_1_2_5, 1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 1_1_2_5, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
 ]
 UpperCAmelCase__     :     List[str]    					=       [
     4_9_1, 1_7_5_5, 3_4, 1_6_1_3, 1_7_5_5, 4_1_7, 9_9_2, 1_6_1_3, 2_2_2, 8_4_2, 1_3_5_3, 1_6_1_3,
     8_4_4, 6_3_2, 1_8_5, 1_6_1_3, 8_4_4, 6_3_2, 1_8_5, 1_6_1_3, 1_8_5, 8_4_2, 6_7_7, 1_6_1_3,
     1_8_5, 1_1_4, 1_3_5_3, 1_6_1_3, 3_0_7, 8_9, 8_4_4, 1_6_1_3, 3_0_7, 1_3_3_2, 2_3_4, 1_9_7_9,
     3_0_7, 8_9, 1_3_5_3, 6_1_6, 3_4, 8_4_2, 1_8_5, 8_4_2, 3_4, 8_4_2, 1_8_5, 8_4_2,
     3_0_7, 1_1_4, 1_8_5, 8_9, 3_4, 1_2_6_8, 1_8_5, 8_9, 3_4, 8_4_2, 1_8_5, 8_9
 ]






 def 						lowercase_    (      self	:Tuple						,					_A	:Union[str, Any]      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      JukeboxTokenizer.from_pretrained(_A      )
       __A													=      tokenizer(**self.metas      )['input_ids']
       return tokens






 @slow
 def 						lowercase_    (      self	:int      )					->      str:







       '''simple docstring'''



       __A													=      JukeboxModel.from_pretrained(self.model_id						,					min_duration=0      ).eval()
       __A													=      self.prepare_inputs(self.model_id      )

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

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

       set_seed(0      )
       __A													=      model._sample(_A						,					_A						,					[2]						,					sample_length=60 * model.priors[2].raw_to_tokens						,					save_results=_A      )
       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 						lowercase_    (      self	:Tuple      )					->      Tuple:







       '''simple docstring'''



       __A													=      JukeboxModel.from_pretrained(self.model_id						,					min_duration=0      ).eval()
       __A													=      [i.cuda() for i in self.prepare_inputs(self.model_id      )]

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






 @slow
 def 						lowercase_    (      self	:Dict      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      'ArthurZ/jukebox_prior_0'
       __A													=      JukeboxPrior.from_pretrained(_A						,					min_duration=0      ).eval().half().to('cuda'      )

       __A													=      self.prepare_inputs(_A      )[0].cuda()
       __A													=      model.get_metadata(_A						,					0						,					7_680						,					0      )
       set_seed(0      )
       __A													=      model.sample(1						,					metadata=_A						,					sample_tokens=60      )
       self.assertIn(outputs[0].cpu().tolist()						,					[self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]      )









import math
import sys





class      snake_case_      :


							"""simple docstring"""





							def __init__(			self				):
												__lowerCAmelCase       =				0
												__lowerCAmelCase       =				[]
												__lowerCAmelCase       =				{}  # To store the pos of node in array




							def 							A__	(			self				):
												return self.cur_size == 0




							def 							A__	(			self       ,    _A				):
												__lowerCAmelCase       =				self.left(_A				)
												__lowerCAmelCase       =				self.right(_A				)
												if lc < self.cur_size and self.array(_A				)[0] < self.array(_A				)[0]:
																	__lowerCAmelCase       =				lc
												else:
																	__lowerCAmelCase       =				idx
												if rc < self.cur_size and self.array(_A				)[0] < self.array(_A				)[0]:
																	__lowerCAmelCase       =				rc
												if smallest != idx:
																	self.swap(_A       ,    _A				)
																	self.min_heapify(_A				)




							def 							A__	(			self       ,    _A				):
												# Inserts a node into the Priority Queue
												__lowerCAmelCase       =				self.cur_size
												self.cur_size += 1
												self.array.append((sys.maxsize, tup[1])				)
												self.decrease_key((sys.maxsize, tup[1])       ,    tup[0]				)




							def 							A__	(			self				):
												# Removes and returns the min element at top of priority queue
												__lowerCAmelCase       =				self.array[0][1]
												__lowerCAmelCase       =				self.array[self.cur_size - 1]
												self.cur_size -= 1
												self.min_heapify(1				)
												del self.pos[min_node]
												return min_node




							def 							A__	(			self       ,    _A				):
												# returns the index of left child
												return 2 * i + 1




							def 							A__	(			self       ,    _A				):
												# returns the index of right child
												return 2 * i + 2




							def 							A__	(			self       ,    _A				):
												# returns the index of parent
												return math.floor(i / 2				)




							def 							A__	(			self       ,    _A       ,    _A				):
												# swaps array elements at indices i and j
												# update the pos{}
												__lowerCAmelCase       =				j
												__lowerCAmelCase       =				i
												__lowerCAmelCase       =				self.array[i]
												__lowerCAmelCase       =				self.array[j]
												__lowerCAmelCase       =				temp




							def 							A__	(			self       ,    _A       ,    _A				):
												__lowerCAmelCase       =				self.pos[tup[1]]
												# assuming the new_d is atmost old_d
												__lowerCAmelCase       =				(new_d, tup[1])
												while idx > 0 and self.array[self.par(_A				)][0] > self.array[idx][0]:
																	self.swap(_A       ,    self.par(_A				)				)
																	__lowerCAmelCase       =				self.par(_A				)





class      snake_case_      :


							"""simple docstring"""





							def __init__(			self       ,    _A				):
												__lowerCAmelCase       =				{}  # To store graph: u -> (v,w)
												__lowerCAmelCase       =				num  # Number of nodes in graph
												# To store the distance from source vertex
												__lowerCAmelCase       =				[0] * self.num_nodes
												__lowerCAmelCase       =				[-1] * self.num_nodes  # To store the path




							def 							A__	(			self       ,    _A       ,    _A       ,    _A				):
												#  Edge going from node u to v and v to u with weight w
												# u (w)-> v, v (w) -> u
												# Check if u already in graph
												if u in self.adjList:
																	self.adjList[u].append((v, w)				)
												else:
																	__lowerCAmelCase       =				[(v, w)]

												# Assuming undirected graph
												if v in self.adjList:
																	self.adjList[v].append((u, w)				)
												else:
																	__lowerCAmelCase       =				[(u, w)]




							def 							A__	(			self				):
												# u -> v(w)
												for u in self.adjList:
																	print(_A       ,    '->'       ,    ' -> '.join(str(F"""{v}({w})"""				) for v, w in self.adjList[u]				)				)




							def 							A__	(			self       ,    _A				):
												# Flush old junk values in par[]
												__lowerCAmelCase       =				[-1] * self.num_nodes
												# src is the source node
												__lowerCAmelCase       =				0
												__lowerCAmelCase       =				PriorityQueue()
												q.insert((0, src)				)  # (dist from src, node)
												for u in self.adjList:
																	if u != src:
																						__lowerCAmelCase       =				sys.maxsize  # Infinity
																						__lowerCAmelCase       =				-1

												while not q.is_empty():
																	__lowerCAmelCase       =				q.extract_min()  # Returns node with the min dist from source
																	# Update the distance of all the neighbours of u and
																	# if their prev dist was INFINITY then push them in Q
																	for v, w in self.adjList[u]:
																						__lowerCAmelCase       =				self.dist[u] + w
																						if self.dist[v] > new_dist:
																											if self.dist[v] == sys.maxsize:
																																q.insert((new_dist, v)				)
																											else:
																																q.decrease_key((self.dist[v], v)       ,    _A				)
																											__lowerCAmelCase       =				new_dist
																											__lowerCAmelCase       =				u

        # Show the shortest distances from src
												self.show_distances(_A				)




							def 							A__	(			self       ,    _A				):
												print(F"""Distance from node: {src}"""				)
												for u in range(self.num_nodes				):
																	print(F"""Node {u} has distance: {self.dist[u]}"""				)




							def 							A__	(			self       ,    _A       ,    _A				):
												# To show the shortest path from src to dest
												# WARNING: Use it *after* calling dijkstra
												__lowerCAmelCase       =				[]
												__lowerCAmelCase       =				0
												__lowerCAmelCase       =				dest
												# Backtracking from dest to src
												while self.par[temp] != -1:
																	path.append(_A				)
																	if temp != src:
																						for v, w in self.adjList[temp]:
																											if v == self.par[temp]:
																																cost += w
																																break
																	__lowerCAmelCase       =				self.par[temp]
												path.append(_A				)
												path.reverse()

												print(F"""----Path to reach {dest} from {src}----"""				)
												for u in path:
																	print(F"""{u}"""       ,    end=' '				)
																	if u != dest:
																						print('-> '       ,    end=''				)

												print('\nTotal cost of path: '       ,    _A				)


if __name__ == "__main__":
		lowerCamelCase   					=  Graph(9)
		graph.add_edge(0, 1, 4)
		graph.add_edge(0, 7, 8)
		graph.add_edge(1, 2, 8)
		graph.add_edge(1, 7, 1_1)
		graph.add_edge(2, 3, 7)
		graph.add_edge(2, 8, 2)
		graph.add_edge(2, 5, 4)
		graph.add_edge(3, 4, 9)
		graph.add_edge(3, 5, 1_4)
		graph.add_edge(4, 5, 1_0)
		graph.add_edge(5, 6, 2)
		graph.add_edge(6, 7, 1)
		graph.add_edge(6, 8, 6)
		graph.add_edge(7, 8, 7)
		graph.show_graph()
		graph.dijkstra(0)
		graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21






