


"""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 	A_       (     unittest.TestCase   ):





  """simple docstring"""

  lowercase		:	Any   =						(JukeboxModel,) if is_torch_available() else ()
  lowercase		:	List[Any]   =						"openai/jukebox-1b-lyrics"
  lowercase		:	List[str]   =						{
      "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
  lowercase		:	int   =						[
      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
  ]

  lowercase		:	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
  ]

  lowercase		:	Tuple   =						[
      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
  ]
  lowercase		:	Optional[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
  ]

  lowercase		:	Optional[int]   =						[
      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
  ]
  lowercase		:	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
  ]

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

  lowercase		:	Union[str, Any]   =						[
      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
  ]
  lowercase		:	str   =						[
      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
  ]
  lowercase		:	Any   =						[
      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
  ]
  lowercase		:	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
  ]
  lowercase		:	List[str]   =						[
      -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
  ]
  lowercase		:	Union[str, Any]   =						[
      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
  ]
  lowercase		:	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
  ]
  lowercase		:	int   =						[
      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 							lowercase_     (						self						)				->					List[str]:
     a   :	int						=    JukeboxTokenizer.from_pretrained(self.model_id						)
     a   :	Union[str, Any]						=    tokenizer(**self.metas						)['input_ids']
     return tokens
  @slow
  def 							lowercase_     (						self						)				->					str:
     a   :	List[Any]						=    JukeboxModel.from_pretrained(self.model_id  ,					min_duration=0						).eval()
     a   :	List[str]						=    self.prepare_inputs()

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

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

     set_seed(0						)
     a   :	Tuple						=    model._sample(__UpperCAmelCase  ,					__UpperCAmelCase  ,					[2]  ,					sample_length=40 * model.priors[2].raw_to_tokens  ,					save_results=__UpperCAmelCase						)
     self.assertIn(zs[2][0].detach().cpu().tolist()  ,					[self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]						)
  @slow
  def 							lowercase_     (						self						)				->					Any:
     a   :	str						=    False
     a   :	Union[str, Any]						=    JukeboxModel.from_pretrained(self.model_id  ,					min_duration=0						).eval()

     a   :	Union[str, Any]						=    self.prepare_inputs()
     set_seed(0						)
     a   :	Tuple						=    [torch.zeros(1  ,					0  ,					dtype=torch.long						) for _ in range(3						)]

     a   :	List[Any]						=    model.priors[0]
     a   :	Dict						=    0
     a   :	int						=    top_prior.get_music_tokens_conds(__UpperCAmelCase  ,					start=__UpperCAmelCase  ,					end=start + top_prior.n_ctx						)
     a   :	Tuple						=    top_prior.get_metadata(labels[0].clone()  ,					__UpperCAmelCase  ,					1_05_83_04  ,					0						)

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

     a	, a	, a   :	int						=    top_prior.get_cond(__UpperCAmelCase  ,					__UpperCAmelCase						)
     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:
     a   :	Any						=    False

     a   :	List[str]						=    JukeboxModel.from_pretrained(self.model_id  ,					min_duration=0						).eval()
     set_seed(0						)
     a   :	Dict						=    torch.rand((1, 51_20, 1)						)
     a   :	Union[str, Any]						=    list(self.prepare_inputs()						)

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

     a   :	Dict						=    torch.cat((zs[0], torch.zeros(1  ,					20_48 - zs[0].shape[-1]						))  ,					dim=-1						).long()
     a   :	List[Any]						=    [upper_a, model.vqvae.encode(__UpperCAmelCase  ,					start_level=1  ,					bs_chunks=waveform.shape[0]						)[0], None]
     a   :	Any						=    model._sample(
         __UpperCAmelCase  ,					__UpperCAmelCase  ,					sample_levels=[1]  ,					save_results=__UpperCAmelCase  ,					sample_length=40 * model.priors[1].raw_to_tokens						)
     torch.testing.assert_allclose(zs[1][0][:40]  ,					torch.tensor(self.EXPECTED_PRIMED_1						)						)

     a   :	Any						=    torch.cat((zs[1], torch.zeros(1  ,					20_48 - zs[1].shape[-1]						))  ,					dim=-1						).long()
     a   :	Optional[int]						=    [upper_a, upper_a, model.vqvae.encode(__UpperCAmelCase  ,					start_level=0  ,					bs_chunks=waveform.shape[0]						)[0]]
     a   :	List[Any]						=    model._sample(
         __UpperCAmelCase  ,					__UpperCAmelCase  ,					sample_levels=[2]  ,					save_results=__UpperCAmelCase  ,					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						)				->					Dict:
     a   :	int						=    JukeboxModel.from_pretrained(self.model_id  ,					min_duration=0						).eval()
     set_seed(0						)
     a   :	Union[str, Any]						=    torch.rand((1, 51_20, 1)						)
     with torch.no_grad():
        a   :	Optional[Any]						=    model.vqvae.encode(__UpperCAmelCase  ,					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   :	Dict						=    model.vqvae.decode(__UpperCAmelCase  ,					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   ):





  """simple docstring"""

  lowercase		:	Tuple   =						(JukeboxModel,) if is_torch_available() else ()
  lowercase		:	Optional[Any]   =						"openai/jukebox-5b-lyrics"
  lowercase		:	str   =						{
      "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
  lowercase		:	Optional[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, 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
  ]
  lowercase		:	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
  ]

  lowercase		:	str   =						[
      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
  ]
  lowercase		:	Dict   =						[
      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
  ]

  lowercase		:	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
  ]
  lowercase		:	List[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, 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
  ]

  lowercase		:	Tuple   =						[
      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
  ]
  lowercase		:	Tuple   =						[
      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
  ]

  lowercase		:	Union[str, Any]   =						[
      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
  ]
  lowercase		:	Optional[int]   =						[
      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 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					List[str]:
     a   :	int						=    JukeboxTokenizer.from_pretrained(__UpperCAmelCase						)
     a   :	Union[str, Any]						=    tokenizer(**self.metas						)['input_ids']
     return tokens
  @slow
  def 							lowercase_     (						self						)				->					Optional[int]:
     a   :	Optional[int]						=    JukeboxModel.from_pretrained(self.model_id  ,					min_duration=0						).eval()
     a   :	Optional[int]						=    self.prepare_inputs(self.model_id						)

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

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

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

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






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

     a   :	Union[str, Any]						=    self.prepare_inputs(__UpperCAmelCase						)[0].cuda()
     a   :	Dict						=    model.get_metadata(__UpperCAmelCase  ,					0  ,					76_80  ,					0						)
     set_seed(0						)
     a   :	List[Any]						=    model.sample(1  ,					metadata=__UpperCAmelCase  ,					sample_tokens=60						)
     self.assertIn(outputs[0].cpu().tolist()  ,					[self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]						)









"""simple docstring"""






import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata




def        A_					(					UpperCAmelCase__				)     ->     str:
   a   :	Optional[int]						=    min(re.search('^ +'       ,   UpperCAmelCase__				).end() if t.startswith(' '				) else 0 for t in string.splitlines()				)
   return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(UpperCAmelCase__				)]				)


SCREAMING_SNAKE_CASE__				:  Union[str, Any]						= "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

SCREAMING_SNAKE_CASE__				:  List[str]						= "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


SCREAMING_SNAKE_CASE__				:  Dict						= "\\n# Begin of markdown\n\nSome cool dataset card\n"


class 	A_       (     unittest.TestCase   ):





  """simple docstring"""

  def 							lowercase_     (						self						)				->					Any:
     with tempfile.TemporaryDirectory() as tmp_dir:
        a   :	Optional[Any]						=    Path(__UpperCAmelCase						) / 'README.md'
        with open(__UpperCAmelCase  ,					'w+'						) as readme_file:
           readme_file.write(__UpperCAmelCase						)
        a   :	str						=    DatasetMetadata.from_readme(__UpperCAmelCase						)
        self.assertDictEqual(__UpperCAmelCase  ,					{'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}						)

        with open(__UpperCAmelCase  ,					'w+'						) as readme_file:
           readme_file.write(__UpperCAmelCase						)
        a   :	Optional[Any]						=    DatasetMetadata.from_readme(__UpperCAmelCase						)
        self.assertDictEqual(__UpperCAmelCase  ,					{}						)

        with open(__UpperCAmelCase  ,					'w+'						) as readme_file:
           readme_file.write(__UpperCAmelCase						)
        a   :	str						=    DatasetMetadata.from_readme(__UpperCAmelCase						)
        self.assertEqual(__UpperCAmelCase  ,					{}						)






  def 							lowercase_     (						self						)				->					Tuple:
     a   :	List[str]						=    _dedent(
         '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '						)
     assert DatasetMetadata.from_yaml_string(__UpperCAmelCase						)

     a   :	Optional[Any]						=    _dedent(
         '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '						)
     with self.assertRaises(__UpperCAmelCase						):
        DatasetMetadata.from_yaml_string(__UpperCAmelCase						)

     a   :	List[Any]						=    _dedent(
         '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '						)
     assert DatasetMetadata.from_yaml_string(__UpperCAmelCase						)





