
"""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 			lowerCamelCase  (					unittest.TestCase      ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE							=     (JukeboxModel,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE							=     "openai/jukebox-1b-lyrics"
  SCREAMING_SNAKE_CASE							=     {
      "artist": "Zac Brown Band",
      "genres": "Country",
      "lyrics": "I met a traveller from an antique land,\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
  SCREAMING_SNAKE_CASE							=     [
      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
  ]

  SCREAMING_SNAKE_CASE							=     [
      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
  ]

  SCREAMING_SNAKE_CASE							=     [
      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
  ]
  SCREAMING_SNAKE_CASE							=     [
      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
  ]

  SCREAMING_SNAKE_CASE							=     [
      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
  ]
  SCREAMING_SNAKE_CASE							=     [
      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
  ]

  SCREAMING_SNAKE_CASE							=     [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]

  SCREAMING_SNAKE_CASE							=     [
      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
  ]
  SCREAMING_SNAKE_CASE							=     [
      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
  ]
  SCREAMING_SNAKE_CASE							=     [
      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
  ]
  SCREAMING_SNAKE_CASE							=     [
      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
  ]
  SCREAMING_SNAKE_CASE							=     [
      -0.04_92, -0.05_24, -0.05_65, -0.06_40, -0.06_86, -0.06_84, -0.06_77, -0.06_64,
      -0.06_05, -0.04_90, -0.03_30, -0.01_68, -0.00_83, -0.00_75, -0.00_51, 0.00_25,
      0.01_36, 0.02_61, 0.03_86, 0.04_97, 0.05_80, 0.05_99, 0.05_83, 0.06_14,
      0.07_40, 0.08_89, 0.10_23, 0.11_62, 0.12_11, 0.12_12, 0.12_51, 0.13_36,
      0.15_02, 0.16_86, 0.18_83, 0.21_48, 0.23_63, 0.24_58, 0.25_07, 0.25_31
  ]
  SCREAMING_SNAKE_CASE							=     [
      0.02_56, -0.05_44, 0.16_00, -0.00_32, 0.10_66, 0.08_25, -0.00_13, 0.34_40,
      0.02_10, 0.04_12, -0.17_77, -0.08_92, -0.01_64, 0.02_85, -0.06_13, -0.06_17,
      -0.01_37, -0.02_01, -0.01_75, 0.02_15, -0.06_27, 0.05_20, -0.07_30, 0.09_70,
      -0.01_00, 0.04_42, -0.05_86, 0.02_07, -0.00_15, -0.00_82
  ]
  SCREAMING_SNAKE_CASE							=     [
      0.04_15, 0.08_77, 0.00_22, -0.00_55, 0.07_51, 0.03_34, 0.03_24, -0.00_68,
      0.00_11, 0.00_17, -0.06_76, 0.06_55, -0.01_43, 0.03_99, 0.03_03, 0.07_43,
      -0.01_68, -0.03_94, -0.11_13, 0.01_24, 0.04_42, 0.02_67, -0.00_03, -0.15_36,
      -0.01_16, -0.18_37, -0.01_80, -0.10_26, -0.07_77, -0.04_56
  ]
  SCREAMING_SNAKE_CASE							=     [
      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    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Tuple						=						JukeboxTokenizer.from_pretrained(self.model_id  )
        UpperCAmelCase__ :       str						=						tokenizer(**self.metas  )["""input_ids"""]
        return tokens



  @slow
  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Union[str, Any]						=						JukeboxModel.from_pretrained(self.model_id       ,					min_duration=0  ).eval()
        UpperCAmelCase__ :       Dict						=						self.prepare_inputs()

        set_seed(0  )
        UpperCAmelCase__ :       int						=						[torch.zeros(1       ,					0       ,					dtype=torch.long  ).cpu() for _ in range(3  )]
        UpperCAmelCase__ :       Optional[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  )
        UpperCAmelCase__ :       List[str]						=						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  )
        UpperCAmelCase__ :       List[str]						=						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    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[str]						=						False
        UpperCAmelCase__ :       List[str]						=						JukeboxModel.from_pretrained(self.model_id       ,					min_duration=0  ).eval()

        UpperCAmelCase__ :       Optional[int]						=						self.prepare_inputs()
        set_seed(0  )
        UpperCAmelCase__ :       List[Any]						=						[torch.zeros(1       ,					0       ,					dtype=torch.long  ) for _ in range(3  )]

        UpperCAmelCase__ :       int						=						model.priors[0]
        UpperCAmelCase__ :       Optional[Any]						=						0
        UpperCAmelCase__ :       List[Any]						=						top_prior.get_music_tokens_conds(UpperCamelCase_       ,					start=UpperCamelCase_       ,					end=start + top_prior.n_ctx  )
        UpperCAmelCase__ :       Dict						=						top_prior.get_metadata(labels[0].clone()       ,					UpperCamelCase_       ,					1058304       ,					0  )

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

        UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__ :       Any						=						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    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[str]						=						False

        UpperCAmelCase__ :       int						=						JukeboxModel.from_pretrained(self.model_id       ,					min_duration=0  ).eval()
        set_seed(0  )
        UpperCAmelCase__ :       str						=						torch.rand((1, 5120, 1)  )
        UpperCAmelCase__ :       Any						=						list(self.prepare_inputs()  )

        UpperCAmelCase__ :       Optional[Any]						=						[model.vqvae.encode(UpperCamelCase_       ,					start_level=2       ,					bs_chunks=waveform.shape[0]  )[0], None, None]
        UpperCAmelCase__ :       Dict						=						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  )  )

        UpperCAmelCase__ :       Union[str, Any]						=						torch.cat((zs[0], torch.zeros(1       ,					2048 - zs[0].shape[-1]  ))       ,					dim=-1  ).long()
        UpperCAmelCase__ :       int						=						[upper_a, model.vqvae.encode(UpperCamelCase_       ,					start_level=1       ,					bs_chunks=waveform.shape[0]  )[0], None]
        UpperCAmelCase__ :       Optional[int]						=						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  )  )

        UpperCAmelCase__ :       Optional[Any]						=						torch.cat((zs[1], torch.zeros(1       ,					2048 - zs[1].shape[-1]  ))       ,					dim=-1  ).long()
        UpperCAmelCase__ :       List[str]						=						[upper_a, upper_a, model.vqvae.encode(UpperCamelCase_       ,					start_level=0       ,					bs_chunks=waveform.shape[0]  )[0]]
        UpperCAmelCase__ :       int						=						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    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[int]						=						JukeboxModel.from_pretrained(self.model_id       ,					min_duration=0  ).eval()
        set_seed(0  )
        UpperCAmelCase__ :       int						=						torch.rand((1, 5120, 1)  )
        with torch.no_grad():
              UpperCAmelCase__ :       Tuple						=						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():
              UpperCAmelCase__ :       Tuple						=						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 			lowerCamelCase  (					unittest.TestCase      ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE							=     (JukeboxModel,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE							=     "openai/jukebox-5b-lyrics"
  SCREAMING_SNAKE_CASE							=     {
      "artist": "Zac Brown Band",
      "genres": "Country",
      "lyrics": "I met a traveller from an antique land,\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
  SCREAMING_SNAKE_CASE							=     [
      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
  ]
  SCREAMING_SNAKE_CASE							=     [
      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
  ]

  SCREAMING_SNAKE_CASE							=     [
      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
  ]
  SCREAMING_SNAKE_CASE							=     [
      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
  ]

  SCREAMING_SNAKE_CASE							=     [
      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
  ]
  SCREAMING_SNAKE_CASE							=     [
      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
  ]

  SCREAMING_SNAKE_CASE							=     [
      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
  ]
  SCREAMING_SNAKE_CASE							=     [
      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
  ]

  SCREAMING_SNAKE_CASE							=     [
      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
  ]
  SCREAMING_SNAKE_CASE							=     [
      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    _a			(self       ,					_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       Union[str, Any]						=						JukeboxTokenizer.from_pretrained(UpperCamelCase_  )
        UpperCAmelCase__ :       Union[str, Any]						=						tokenizer(**self.metas  )["""input_ids"""]
        return tokens



  @slow
  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       str						=						JukeboxModel.from_pretrained(self.model_id       ,					min_duration=0  ).eval()
        UpperCAmelCase__ :       List[Any]						=						self.prepare_inputs(self.model_id  )

        set_seed(0  )
        UpperCAmelCase__ :       Optional[Any]						=						[torch.zeros(1       ,					0       ,					dtype=torch.long  ).cpu() for _ in range(3  )]
        UpperCAmelCase__ :       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  )
        UpperCAmelCase__ :       Optional[int]						=						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  )
        UpperCAmelCase__ :       List[str]						=						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    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[int]						=						JukeboxModel.from_pretrained(self.model_id       ,					min_duration=0  ).eval()
        UpperCAmelCase__ :       Union[str, Any]						=						[i.cuda() for i in self.prepare_inputs(self.model_id  )]

        set_seed(0  )
        model.priors[0].cuda()
        UpperCAmelCase__ :       List[str]						=						[torch.zeros(1       ,					0       ,					dtype=torch.long  ).cuda() for _ in range(3  )]
        UpperCAmelCase__ :       str						=						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()
        UpperCAmelCase__ :       Dict						=						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()
        UpperCAmelCase__ :       Dict						=						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    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[Any]						=						"""ArthurZ/jukebox_prior_0"""
        UpperCAmelCase__ :       Any						=						JukeboxPrior.from_pretrained(UpperCamelCase_       ,					min_duration=0  ).eval().half().to("""cuda"""  )

        UpperCAmelCase__ :       List[str]						=						self.prepare_inputs(UpperCamelCase_  )[0].cuda()
        UpperCAmelCase__ :       Optional[Any]						=						model.get_metadata(UpperCamelCase_       ,					0       ,					7680       ,					0  )
        set_seed(0  )
        UpperCAmelCase__ :       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 tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 			lowerCamelCase  (					unittest.TestCase      ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE							=     (ScoreSdeVeScheduler,)
  SCREAMING_SNAKE_CASE							=     ()



  @property
  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[Any]						=						4
        UpperCAmelCase__ :       Tuple						=						3
        UpperCAmelCase__ :       Optional[Any]						=						8
        UpperCAmelCase__ :       Dict						=						8

        UpperCAmelCase__ :       List[Any]						=						torch.rand((batch_size, num_channels, height, width)  )

        return sample



  @property
  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Tuple						=						4
        UpperCAmelCase__ :       Optional[Any]						=						3
        UpperCAmelCase__ :       List[str]						=						8
        UpperCAmelCase__ :       Dict						=						8

        UpperCAmelCase__ :       List[str]						=						batch_size * num_channels * height * width
        UpperCAmelCase__ :       Any						=						torch.arange(_lowerCamelCase  )
        UpperCAmelCase__ :       Any						=						sample.reshape(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
        UpperCAmelCase__ :       Dict						=						sample / num_elems
        UpperCAmelCase__ :       Optional[int]						=						sample.permute(3       ,					0       ,					1       ,					2  )

        return sample



  def    _a			(self  ):


        """simple docstring"""

        def model(_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
              return sample * t / (t + 1)

        return model



  def    _a			(self       ,					**_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[str]						=						{
            """num_train_timesteps""": 2000,
            """snr""": 0.15,
            """sigma_min""": 0.01,
            """sigma_max""": 1348,
            """sampling_eps""": 1e-5,
        }

        config.update(**_lowerCamelCase  )
        return config



  def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       str						=						dict(self.forward_default_kwargs  )

        for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__ :       int						=						self.dummy_sample
              UpperCAmelCase__ :       Tuple						=						0.1 * sample

              UpperCAmelCase__ :       int						=						self.get_scheduler_config(**_lowerCamelCase  )
              UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_lowerCamelCase  )
                    UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )

              UpperCAmelCase__ :       Tuple						=						scheduler.step_pred(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
              UpperCAmelCase__ :       Optional[int]						=						new_scheduler.step_pred(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample

              assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

              UpperCAmelCase__ :       Optional[int]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
              UpperCAmelCase__ :       Union[str, Any]						=						new_scheduler.step_correct(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample

              assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"



  def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )
        kwargs.update(_lowerCamelCase  )

        for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__ :       List[str]						=						self.dummy_sample
              UpperCAmelCase__ :       List[str]						=						0.1 * sample

              UpperCAmelCase__ :       Optional[Any]						=						self.get_scheduler_config()
              UpperCAmelCase__ :       Optional[Any]						=						scheduler_class(**_lowerCamelCase  )

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_lowerCamelCase  )
                    UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )

              UpperCAmelCase__ :       Optional[int]						=						scheduler.step_pred(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
              UpperCAmelCase__ :       Any						=						new_scheduler.step_pred(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample

              assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

              UpperCAmelCase__ :       Optional[Any]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
              UpperCAmelCase__ :       Dict						=						new_scheduler.step_correct(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample

              assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"



  def    _a			(self  ):


        """simple docstring"""

        for timesteps in [10, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_lowerCamelCase  )



  def    _a			(self  ):


        """simple docstring"""

        for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]       ,					[1, 100, 1000]  ):
              self.check_over_configs(sigma_min=_lowerCamelCase       ,					sigma_max=_lowerCamelCase  )



  def    _a			(self  ):


        """simple docstring"""

        for t in [0.1, 0.5, 0.75]:
              self.check_over_forward(time_step=_lowerCamelCase  )



  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[Any]						=						dict(self.forward_default_kwargs  )

        UpperCAmelCase__ :       int						=						self.scheduler_classes[0]
        UpperCAmelCase__ :       int						=						self.get_scheduler_config()
        UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )

        UpperCAmelCase__ :       List[str]						=						3

        UpperCAmelCase__ :       List[Any]						=						self.dummy_model()
        UpperCAmelCase__ :       int						=						self.dummy_sample_deter

        scheduler.set_sigmas(_lowerCamelCase  )
        scheduler.set_timesteps(_lowerCamelCase  )
        UpperCAmelCase__ :       Tuple						=						torch.manual_seed(0  )

        for i, t in enumerate(scheduler.timesteps  ):
              UpperCAmelCase__ :       Tuple						=						scheduler.sigmas[i]

              for _ in range(scheduler.config.correct_steps  ):
                    with torch.no_grad():
                          UpperCAmelCase__ :       Optional[int]						=						model(_lowerCamelCase       ,					_lowerCamelCase  )
                    UpperCAmelCase__ :       List[str]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  ).prev_sample

              with torch.no_grad():
                    UpperCAmelCase__ :       Tuple						=						model(_lowerCamelCase       ,					_lowerCamelCase  )

              UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  )
              UpperCAmelCase__				,		UpperCAmelCase__ :       List[str]						=						output.prev_sample, output.prev_sample_mean

        UpperCAmelCase__ :       int						=						torch.sum(torch.abs(_lowerCamelCase  )  )
        UpperCAmelCase__ :       str						=						torch.mean(torch.abs(_lowerCamelCase  )  )

        assert np.isclose(result_sum.item()       ,					14_372_758_528.0  )
        assert np.isclose(result_mean.item()       ,					18_714_530.0  )




  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )

        UpperCAmelCase__ :       Union[str, Any]						=						kwargs.pop("""num_inference_steps"""       ,					_lowerCamelCase  )

        for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__ :       Dict						=						self.get_scheduler_config()
              UpperCAmelCase__ :       List[Any]						=						scheduler_class(**_lowerCamelCase  )

              UpperCAmelCase__ :       str						=						self.dummy_sample
              UpperCAmelCase__ :       Tuple						=						0.1 * sample

              if num_inference_steps is not None and hasattr(_lowerCamelCase       ,					"""set_timesteps"""  ):
                    scheduler.set_timesteps(_lowerCamelCase  )
              elif num_inference_steps is not None and not hasattr(_lowerCamelCase       ,					"""set_timesteps"""  ):
                    UpperCAmelCase__ :       Dict						=						num_inference_steps

              UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					0       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
              UpperCAmelCase__ :       int						=						scheduler.step_pred(_lowerCamelCase       ,					1       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample

              self.assertEqual(output_a.shape       ,					sample.shape  )
              self.assertEqual(output_a.shape       ,					output_a.shape  )

