






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	):



     """simple docstring"""



     __snake_case		:				Optional[int]  					=(JukeboxModel,) if is_torch_available() else ()
     __snake_case		:				str  					='openai/jukebox-1b-lyrics'
     __snake_case		:				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
     __snake_case		:				List[str]  					=[
         18_64, 15_36, 12_13, 18_70, 13_57, 15_36, 5_19, 8_80, 13_23, 7_89, 10_82, 5_34,
         10_00, 14_45, 11_05, 11_30, 9_67, 5_15, 14_34, 16_20, 5_34, 14_95, 2_83, 14_45,
         3_33, 13_07, 5_39, 16_31, 15_28, 3_75, 14_34, 6_73, 6_27, 7_10, 7_78, 18_83,
         14_05, 12_76, 14_55, 12_28
     ]

     __snake_case		:				int  					=[
         14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
         6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
         6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
         6_53, 6_53, 6_53, 6_53
     ]

     __snake_case		:				Dict  					=[
         11_25, 17_51, 6_97, 17_76, 11_41, 14_76, 3_91, 6_97, 11_25, 6_84, 8_67, 4_16,
         8_44, 13_72, 12_74, 7_17, 12_74, 8_44, 12_99, 14_19, 6_97, 13_70, 3_17, 11_25,
         1_91, 14_40, 13_70, 14_40, 13_70, 2_82, 16_21, 13_70, 3_68, 3_49, 8_67, 18_72,
         12_62, 8_69, 17_28, 7_47
     ]
     __snake_case		:				List[Any]  					=[
         4_16, 4_16, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
         4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
         4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
         4_16, 4_16, 4_16, 4_16
     ]

     __snake_case		:				str  					=[
         17_55, 8_42, 3_07, 18_43, 10_22, 13_95, 2_34, 15_54, 8_06, 7_39, 10_22, 4_42,
         6_16, 5_56, 2_68, 14_99, 9_33, 4_57, 14_40, 18_37, 7_55, 9_85, 3_08, 9_02,
         2_93, 14_43, 16_71, 11_41, 15_33, 5_55, 15_62, 10_61, 2_87, 4_17, 10_22, 20_08,
         11_86, 10_15, 17_77, 2_68
     ]
     __snake_case		:				Optional[Any]  					=[
         8_54, 8_42, 13_53, 1_14, 13_53, 8_42, 1_85, 8_42, 1_85, 1_14, 5_91, 8_42,
         1_85, 4_17, 1_85, 8_42, 3_07, 8_42, 5_91, 8_42, 1_85, 8_42, 3_07, 8_42,
         5_91, 8_42, 13_53, 8_42, 1_85, 8_42, 5_91, 8_42, 5_91, 1_14, 5_91, 8_42,
         1_85, 8_42, 5_91, 89
     ]

     __snake_case		:				Any  					=[1_05_83_04, 0, 78_64_32, 71_69, 5_07, 76, 27, 40, 30, 76]

     __snake_case		:				int  					=[
         3_90, 11_60, 10_02, 19_07, 17_88, 17_88, 17_88, 19_07, 10_02, 10_02, 18_54, 10_02,
         10_02, 10_02, 10_02, 10_02, 10_02, 11_60, 11_60, 16_06, 5_96, 5_96, 11_60, 10_02,
         15_16, 5_96, 10_02, 10_02, 10_02, 19_07, 17_88, 17_88, 17_88, 18_54, 17_88, 19_07,
         19_07, 17_88, 5_96, 16_26
     ]
     __snake_case		:				Optional[int]  					=[
         12_36, 16_68, 14_84, 19_20, 18_48, 14_09, 1_39, 8_64, 18_28, 12_72, 15_99, 8_24,
         16_72, 1_39, 5_55, 14_84, 8_24, 19_20, 5_55, 5_96, 15_79, 15_99, 12_31, 15_99,
         16_37, 14_07, 2_12, 8_24, 15_99, 1_16, 14_33, 8_24, 2_58, 15_99, 14_33, 18_95,
         10_63, 14_33, 14_33, 15_99
     ]
     __snake_case		:				int  					=[
         16_84, 18_73, 11_19, 11_89, 3_95, 6_11, 19_01, 9_72, 8_90, 13_37, 13_92, 19_27,
         96, 9_72, 6_72, 7_80, 11_19, 8_90, 1_58, 7_71, 10_73, 19_27, 3_53, 13_31,
         12_69, 14_59, 13_33, 16_45, 8_12, 15_77, 13_37, 6_06, 3_53, 9_81, 14_66, 6_19,
         1_97, 3_91, 3_02, 19_30
     ]
     __snake_case		:				Optional[int]  					=[
         3_90, 11_60, 10_02, 19_07, 17_88, 17_88, 17_88, 19_07, 10_02, 10_02, 18_54, 10_02,
         10_02, 10_02, 10_02, 10_02, 10_02, 11_60, 11_60, 16_06, 5_96, 5_96, 11_60, 10_02,
         15_16, 5_96, 10_02, 10_02, 10_02, 19_07, 17_88, 17_88, 17_88, 18_54, 17_88, 19_07,
         19_07, 17_88, 5_96, 16_26
     ]
     __snake_case		:				Any  					=[
         -0.0492, -0.0524, -0.0565, -0.0640, -0.0686, -0.0684, -0.0677, -0.0664,
         -0.0605, -0.0490, -0.0330, -0.0168, -0.0083, -0.0075, -0.0051, 0.0025,
         0.0136, 0.0261, 0.0386, 0.0497, 0.0580, 0.0599, 0.0583, 0.0614,
         0.0740, 0.0889, 0.1023, 0.1162, 0.1211, 0.1212, 0.1251, 0.1336,
         0.1502, 0.1686, 0.1883, 0.2148, 0.2363, 0.2458, 0.2507, 0.2531
     ]
     __snake_case		:				Dict  					=[
         0.0256, -0.0544, 0.1600, -0.0032, 0.1066, 0.0825, -0.0013, 0.3440,
         0.0210, 0.0412, -0.1777, -0.0892, -0.0164, 0.0285, -0.0613, -0.0617,
         -0.0137, -0.0201, -0.0175, 0.0215, -0.0627, 0.0520, -0.0730, 0.0970,
         -0.0100, 0.0442, -0.0586, 0.0207, -0.0015, -0.0082
     ]
     __snake_case		:				List[str]  					=[
         0.0415, 0.0877, 0.0022, -0.0055, 0.0751, 0.0334, 0.0324, -0.0068,
         0.0011, 0.0017, -0.0676, 0.0655, -0.0143, 0.0399, 0.0303, 0.0743,
         -0.0168, -0.0394, -0.1113, 0.0124, 0.0442, 0.0267, -0.0003, -0.1536,
         -0.0116, -0.1837, -0.0180, -0.1026, -0.0777, -0.0456
     ]
     __snake_case		:				Optional[Any]  					=[
         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     __UpperCamelCase						(		self					:	Tuple)							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		JukeboxTokenizer.from_pretrained(self.model_id)
      SCREAMING_SNAKE_CASE				    =		tokenizer(**self.metas)['input_ids']
      return tokens





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		JukeboxModel.from_pretrained(self.model_id  ,  min_duration=0).eval()
      SCREAMING_SNAKE_CASE				    =		self.prepare_inputs()

      set_seed(0)
      SCREAMING_SNAKE_CASE				    =		[torch.zeros(1  ,  0  ,  dtype=torch.long).cpu() for _ in range(3)]
      SCREAMING_SNAKE_CASE				    =		model._sample(_A  ,  _A  ,  [0]  ,  sample_length=4_0 * 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)
      SCREAMING_SNAKE_CASE				    =		model._sample(_A  ,  _A  ,  [1]  ,  sample_length=4_0 * 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)
      SCREAMING_SNAKE_CASE				    =		model._sample(_A  ,  _A  ,  [2]  ,  sample_length=4_0 * 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     __UpperCamelCase						(		self					:	Any)							->	str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		False
      SCREAMING_SNAKE_CASE				    =		JukeboxModel.from_pretrained(self.model_id  ,  min_duration=0).eval()

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

      SCREAMING_SNAKE_CASE				    =		model.priors[0]
      SCREAMING_SNAKE_CASE				    =		0
      SCREAMING_SNAKE_CASE				    =		top_prior.get_music_tokens_conds(_A  ,  start=_A  ,  end=start + top_prior.n_ctx)
      SCREAMING_SNAKE_CASE				    =		top_prior.get_metadata(labels[0].clone()  ,  _A  ,  1_0_5_8_3_0_4  ,  0)

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

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





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		False

      SCREAMING_SNAKE_CASE				    =		JukeboxModel.from_pretrained(self.model_id  ,  min_duration=0).eval()
      set_seed(0)
      SCREAMING_SNAKE_CASE				    =		torch.rand((1, 5_1_2_0, 1))
      SCREAMING_SNAKE_CASE				    =		list(self.prepare_inputs())

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

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

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





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

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

@require_torch
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     __snake_case		:				int  					=(JukeboxModel,) if is_torch_available() else ()
     __snake_case		:				Optional[int]  					='openai/jukebox-5b-lyrics'
     __snake_case		:				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
     __snake_case		:				Union[str, Any]  					=[
         14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
         6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
         6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
         6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
         14_89, 14_89, 14_89, 14_89, 11_50, 18_53, 15_09, 11_50, 13_57, 15_09, 6, 12_72
     ]
     __snake_case		:				Dict  					=[
         14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
         6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
         6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
         6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
         6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53
     ]

     __snake_case		:				Dict  					=[
         11_25, 4_16, 11_25, 11_25, 11_25, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16,
         4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
         4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
         4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
         4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
     ]
     __snake_case		:				Optional[int]  					=[
         4_16, 4_16, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
         4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
         4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
         4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
         4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
     ]

     __snake_case		:				List[str]  					=[
         17_55, 10_61, 2_34, 17_55, 10_61, 17_55, 1_85, 2_90, 3_07, 3_07, 6_16, 6_16,
         6_16, 6_16, 6_16, 6_16, 3_07, 2_90, 4_17, 17_55, 2_34, 17_55, 1_85, 2_90,
         2_90, 2_90, 3_07, 6_16, 6_16, 6_16, 6_16, 6_16, 2_90, 2_34, 2_34, 17_55,
         2_34, 2_34, 17_55, 2_34, 1_85, 1_85, 3_07, 6_16, 6_16, 6_16, 6_16, 2_90,
         17_55, 17_55, 17_55, 2_34, 2_34, 17_55, 15_72, 2_90, 3_07, 6_16, 34, 6_16
     ]
     __snake_case		:				Tuple  					=[
         8_54, 8_42, 13_53, 1_14, 13_53, 8_42, 1_85, 8_42, 1_85, 1_14, 5_91, 8_42, 1_85,
         4_17, 1_85, 8_42, 3_07, 8_42, 5_91, 8_42, 1_85, 8_42, 1_85, 8_42, 5_91, 8_42,
         13_53, 8_42, 1_85, 8_42, 5_91, 8_42, 5_91, 1_14, 5_91, 8_42, 1_85, 8_42, 5_91,
         89, 5_91, 8_42, 5_91, 8_42, 5_91, 4_17, 13_72, 8_42, 13_72, 8_42, 34, 8_42,
         1_85, 89, 5_91, 8_42, 1_85, 8_42, 5_91, 6_32
     ]

     __snake_case		:				List[Any]  					=[
         14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
         6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
         6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
         6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
         6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53
     ]
     __snake_case		:				Optional[Any]  					=[
         14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
         6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
         6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 18_53, 11_77, 15_36, 12_28,
         7_10, 4_75, 14_89, 12_29, 12_24, 2_31, 12_24, 2_52, 14_34, 6_53, 4_75,
         11_06, 18_77, 15_99, 12_28, 16_00, 16_83, 11_82, 18_53, 4_75, 18_64,
         2_52, 12_29, 14_34, 20_01
     ]

     __snake_case		:				List[str]  					=[
         11_25, 11_25, 4_16, 11_25, 11_25, 4_16, 11_25, 11_25, 4_16, 4_16, 11_25, 4_16,
         4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
         4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
         4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
         4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
     ]
     __snake_case		:				List[str]  					=[
         4_91, 17_55, 34, 16_13, 17_55, 4_17, 9_92, 16_13, 2_22, 8_42, 13_53, 16_13,
         8_44, 6_32, 1_85, 16_13, 8_44, 6_32, 1_85, 16_13, 1_85, 8_42, 6_77, 16_13,
         1_85, 1_14, 13_53, 16_13, 3_07, 89, 8_44, 16_13, 3_07, 13_32, 2_34, 19_79,
         3_07, 89, 13_53, 6_16, 34, 8_42, 1_85, 8_42, 34, 8_42, 1_85, 8_42,
         3_07, 1_14, 1_85, 89, 34, 12_68, 1_85, 89, 34, 8_42, 1_85, 89
     ]





     def     __UpperCamelCase						(		self					:	Tuple  ,  lowerCAmelCase__					:	Union[str, Any])							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		JukeboxTokenizer.from_pretrained(_A)
      SCREAMING_SNAKE_CASE				    =		tokenizer(**self.metas)['input_ids']
      return tokens





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		JukeboxModel.from_pretrained(self.model_id  ,  min_duration=0).eval()
      SCREAMING_SNAKE_CASE				    =		self.prepare_inputs(self.model_id)

      set_seed(0)
      SCREAMING_SNAKE_CASE				    =		[torch.zeros(1  ,  0  ,  dtype=torch.long).cpu() for _ in range(3)]
      SCREAMING_SNAKE_CASE				    =		model._sample(_A  ,  _A  ,  [0]  ,  sample_length=6_0 * 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)
      SCREAMING_SNAKE_CASE				    =		model._sample(_A  ,  _A  ,  [1]  ,  sample_length=6_0 * 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)
      SCREAMING_SNAKE_CASE				    =		model._sample(_A  ,  _A  ,  [2]  ,  sample_length=6_0 * 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     __UpperCamelCase						(		self					:	Tuple)							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		JukeboxModel.from_pretrained(self.model_id  ,  min_duration=0).eval()
      SCREAMING_SNAKE_CASE				    =		[i.cuda() for i in self.prepare_inputs(self.model_id)]

      set_seed(0)
      model.priors[0].cuda()
      SCREAMING_SNAKE_CASE				    =		[torch.zeros(1  ,  0  ,  dtype=torch.long).cuda() for _ in range(3)]
      SCREAMING_SNAKE_CASE				    =		model._sample(_A  ,  _A  ,  [0]  ,  sample_length=6_0 * 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()
      SCREAMING_SNAKE_CASE				    =		model._sample(_A  ,  _A  ,  [1]  ,  sample_length=6_0 * 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()
      SCREAMING_SNAKE_CASE				    =		model._sample(_A  ,  _A  ,  [2]  ,  sample_length=6_0 * 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     __UpperCamelCase						(		self					:	Dict)							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		'ArthurZ/jukebox_prior_0'
      SCREAMING_SNAKE_CASE				    =		JukeboxPrior.from_pretrained(_A  ,  min_duration=0).eval().half().to('cuda')

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






'''simple docstring'''





import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      # Initialise PyTorch model
      __A													=      RoFormerConfig.from_json_file(UpperCAmelCase						)
      print(f'Building PyTorch model from configuration: {config}'						)
      __A													=      RoFormerForMaskedLM(UpperCAmelCase						)

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)

      # Save pytorch-model
      print(f'Save PyTorch model to {pytorch_dump_path}'						)
      torch.save(model.state_dict()   ,	UpperCAmelCase   ,	_use_new_zipfile_serialization=UpperCAmelCase						)


if __name__ == "__main__":
 a__		: Tuple  							=							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--bert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained BERT model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a__		: Union[str, Any]  							=							parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






