

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"""




  UpperCAmelCase__ :	Tuple         =(JukeboxModel,) if is_torch_available() else ()
  UpperCAmelCase__ :	Union[str, Any]         ="""openai/jukebox-1b-lyrics"""
  UpperCAmelCase__ :	int         ={
      """artist""": """Zac Brown Band""",
      """genres""": """Country""",
      """lyrics""": """I met a traveller from an antique land,
    Who said \"Two vast and trunkless legs of stone
    Stand in the desert. . . . Near them, on the sand,
    Half sunk a shattered visage lies, whose frown,
    And wrinkled lip, and sneer of cold command,
    Tell that its sculptor well those passions read
    Which yet survive, stamped on these lifeless things,
    The hand that mocked them, and the heart that fed;
    And on the pedestal, these words appear:
    My name is Ozymandias, King of Kings;
    Look on my Works, ye Mighty, and despair!
    Nothing beside remains. Round the decay
    Of that colossal Wreck, boundless and bare
    The lone and level sands stretch far away
    """,
  }
  # fmt: off
  UpperCAmelCase__ :	List[Any]         =[
      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__ :	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
  ]

  UpperCAmelCase__ :	int         =[
      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[str]         =[
      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__ :	List[Any]         =[
      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__ :	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__ :	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__ :	Union[str, Any]         =[
      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__ :	List[str]         =[
      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__ :	int         =[
      -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
  ]
  UpperCAmelCase__ :	str         =[
      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
  ]
  UpperCAmelCase__ :	int         =[
      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
  ]
  UpperCAmelCase__ :	List[str]         =[
      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      :	Dict						)   ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[str]					       =				JukeboxTokenizer.from_pretrained(self.model_id						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				tokenizer(**self.metas						)["""input_ids"""]
        return tokens


  @slow
  def 	_lowercase (     self      :	Union[str, Any]						)   ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				JukeboxModel.from_pretrained(self.model_id			,			min_duration=0						).eval()
        SCREAMING_SNAKE_CASE	: Dict					       =				self.prepare_inputs()

        set_seed(0						)
        SCREAMING_SNAKE_CASE	: str					       =				[torch.zeros(1			,			0			,			dtype=torch.long						).cpu() for _ in range(3						)]
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				model._sample(UpperCAmelCase__			,			UpperCAmelCase__			,			[0]			,			sample_length=4_0 * 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						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				model._sample(UpperCAmelCase__			,			UpperCAmelCase__			,			[1]			,			sample_length=4_0 * 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						)
        SCREAMING_SNAKE_CASE	: int					       =				model._sample(UpperCAmelCase__			,			UpperCAmelCase__			,			[2]			,			sample_length=4_0 * 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      :	List[Any]						)   ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				False
        SCREAMING_SNAKE_CASE	: int					       =				JukeboxModel.from_pretrained(self.model_id			,			min_duration=0						).eval()

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

        SCREAMING_SNAKE_CASE	: str					       =				model.priors[0]
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				0
        SCREAMING_SNAKE_CASE	: List[Any]					       =				top_prior.get_music_tokens_conds(UpperCAmelCase__			,			start=UpperCAmelCase__			,			end=start + top_prior.n_ctx						)
        SCREAMING_SNAKE_CASE	: str					       =				top_prior.get_metadata(labels[0].clone()			,			UpperCAmelCase__			,			1_0_5_8_3_0_4			,			0						)

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

        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: int					       =				top_prior.get_cond(UpperCAmelCase__			,			UpperCAmelCase__						)
        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 	_lowercase (     self      :	Union[str, Any]						)   ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[str]					       =				False

        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				JukeboxModel.from_pretrained(self.model_id			,			min_duration=0						).eval()
        set_seed(0						)
        SCREAMING_SNAKE_CASE	: int					       =				torch.rand((1, 5_1_2_0, 1)						)
        SCREAMING_SNAKE_CASE	: int					       =				list(self.prepare_inputs()						)

        SCREAMING_SNAKE_CASE	: List[Any]					       =				[model.vqvae.encode(UpperCAmelCase__			,			start_level=2			,			bs_chunks=waveform.shape[0]						)[0], None, None]
        SCREAMING_SNAKE_CASE	: List[str]					       =				model._sample(
            UpperCAmelCase__			,			UpperCAmelCase__			,			sample_levels=[0]			,			save_results=UpperCAmelCase__			,			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	: Any					       =				torch.cat((zs[0], torch.zeros(1			,			2_0_4_8 - zs[0].shape[-1]						))			,			dim=-1						).long()
        SCREAMING_SNAKE_CASE	: Tuple					       =				[upper_a, model.vqvae.encode(UpperCAmelCase__			,			start_level=1			,			bs_chunks=waveform.shape[0]						)[0], None]
        SCREAMING_SNAKE_CASE	: Tuple					       =				model._sample(
            UpperCAmelCase__			,			UpperCAmelCase__			,			sample_levels=[1]			,			save_results=UpperCAmelCase__			,			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	: Any					       =				torch.cat((zs[1], torch.zeros(1			,			2_0_4_8 - zs[1].shape[-1]						))			,			dim=-1						).long()
        SCREAMING_SNAKE_CASE	: str					       =				[upper_a, upper_a, model.vqvae.encode(UpperCAmelCase__			,			start_level=0			,			bs_chunks=waveform.shape[0]						)[0]]
        SCREAMING_SNAKE_CASE	: Any					       =				model._sample(
            UpperCAmelCase__			,			UpperCAmelCase__			,			sample_levels=[2]			,			save_results=UpperCAmelCase__			,			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 	_lowercase (     self      :	Optional[Any]						)   ->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[str]					       =				JukeboxModel.from_pretrained(self.model_id			,			min_duration=0						).eval()
        set_seed(0						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				torch.rand((1, 5_1_2_0, 1)						)
        with torch.no_grad():
              SCREAMING_SNAKE_CASE	: Dict					       =				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():
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				model.vqvae.decode(UpperCAmelCase__			,			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"""




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

  # fmt: off
  UpperCAmelCase__ :	List[str]         =[
      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__ :	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,
      6_5_3, 6_5_3, 6_5_3, 6_5_3, 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__ :	Union[str, Any]         =[
      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__ :	Tuple         =[
      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__ :	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__ :	Optional[int]         =[
      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__ :	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, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
      6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
      6_5_3, 6_5_3, 6_5_3, 6_5_3, 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__ :	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__ :	Tuple         =[
      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__ :	Dict         =[
      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      :	List[str]			,			UpperCAmelCase__      :	Any						)   ->List[Any]:
        """simple docstring"""







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


  @slow
  def 	_lowercase (     self      :	Any						)   ->Optional[Any]:
        """simple docstring"""







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

        set_seed(0						)
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				[torch.zeros(1			,			0			,			dtype=torch.long						).cpu() for _ in range(3						)]
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				model._sample(UpperCAmelCase__			,			UpperCAmelCase__			,			[0]			,			sample_length=6_0 * 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						)
        SCREAMING_SNAKE_CASE	: str					       =				model._sample(UpperCAmelCase__			,			UpperCAmelCase__			,			[1]			,			sample_length=6_0 * 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						)
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				model._sample(UpperCAmelCase__			,			UpperCAmelCase__			,			[2]			,			sample_length=6_0 * 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[str]						)   ->Tuple:
        """simple docstring"""







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

        set_seed(0						)
        model.priors[0].cuda()
        SCREAMING_SNAKE_CASE	: List[str]					       =				[torch.zeros(1			,			0			,			dtype=torch.long						).cuda() for _ in range(3						)]
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				model._sample(UpperCAmelCase__			,			UpperCAmelCase__			,			[0]			,			sample_length=6_0 * 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()
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				model._sample(UpperCAmelCase__			,			UpperCAmelCase__			,			[1]			,			sample_length=6_0 * 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()
        SCREAMING_SNAKE_CASE	: Any					       =				model._sample(UpperCAmelCase__			,			UpperCAmelCase__			,			[2]			,			sample_length=6_0 * 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      :	int						)   ->Optional[Any]:
        """simple docstring"""







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

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







import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


UpperCAmelCase__	:       Tuple			      =      """\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
"""

UpperCAmelCase__	:       Union[str, Any]			      =      """\
FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
"""


UpperCAmelCase__	:       str			      =      """
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric(\"frugalscore\")
    >>> results = frugalscore.compute(predictions=['hello there', 'huggingface'], references=['hello world', 'hugging face'])
    >>> print([round(s, 3) for s in results[\"scores\"]])
    [0.631, 0.645]
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,     _KWARGS_DESCRIPTION    )
class     a__    (				datasets.Metric    ):







  """simple docstring"""




  def 	_lowercase (     self      :	Any						)   ->Optional[int]:
        """simple docstring"""







        return datasets.MetricInfo(
            description=_DESCRIPTION			,			citation=_CITATION			,			inputs_description=_KWARGS_DESCRIPTION			,			features=datasets.Features(
                {
                    """predictions""": datasets.Value("""string"""						),
                    """references""": datasets.Value("""string"""						),
                }						)			,			homepage="""https://github.com/moussaKam/FrugalScore"""			,			)


  def 	_lowercase (     self      :	Dict			,			UpperCAmelCase__      :	Tuple						)   ->Any:
        """simple docstring"""







        if self.config_name == "default":
              SCREAMING_SNAKE_CASE	: int					       =				"""moussaKam/frugalscore_tiny_bert-base_bert-score"""
        else:
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.config_name
        SCREAMING_SNAKE_CASE	: Tuple					       =				AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				AutoTokenizer.from_pretrained(UpperCAmelCase__						)




  def 	_lowercase (     self      :	Union[str, Any]			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	Optional[Any]			,			UpperCAmelCase__      :	str=3_2			,			UpperCAmelCase__      :	Optional[int]=1_2_8			,			UpperCAmelCase__      :	Optional[int]=None			,			)   ->Any:
        """simple docstring"""







        assert len(UpperCAmelCase__						) == len(
            UpperCAmelCase__						), "predictions and references should have the same number of sentences."
        if device is not None:
              assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
        else:
              SCREAMING_SNAKE_CASE	: List[str]					       =				"""gpu""" if torch.cuda.is_available() else """cpu"""
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				TrainingArguments(
            """trainer"""			,			fpaa=(device == """gpu""")			,			per_device_eval_batch_size=UpperCAmelCase__			,			report_to="""all"""			,			no_cuda=(device == """cpu""")			,			log_level="""warning"""			,			)
        SCREAMING_SNAKE_CASE	: int					       =				{"""sentence1""": predictions, """sentence2""": references}
        SCREAMING_SNAKE_CASE	: List[str]					       =				datasets.Dataset.from_dict(UpperCAmelCase__						)

        def tokenize_function(UpperCAmelCase__      :	Any						):
              return self.tokenizer(
                  data["""sentence1"""]			,			data["""sentence2"""]			,			max_length=UpperCAmelCase__			,			truncation=UpperCAmelCase__			,			padding=UpperCAmelCase__						)

        SCREAMING_SNAKE_CASE	: Dict					       =				raw_datasets.map(UpperCAmelCase__			,			batched=UpperCAmelCase__						)
        tokenized_datasets.remove_columns(["""sentence1""", """sentence2"""]						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				Trainer(self.model			,			UpperCAmelCase__			,			tokenizer=self.tokenizer						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				trainer.predict(UpperCAmelCase__						)
        return {"scores": list(predictions.predictions.squeeze(-1						)						)}




