




import unittest
from unittest import skip

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow
from transformers.trainer_utils import set_seed


if is_torch_available():
						import torch

						from transformers import JukeboxModel, JukeboxPrior, JukeboxTokenizer







@require_torch
class 				UpperCAmelCase(					unittest.TestCase					):



	"""simple docstring"""






	a     : Union[str, Any]	  =					(JukeboxModel,) if is_torch_available() else ()
	a     : str	  =					"""openai/jukebox-1b-lyrics"""
	a     : 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
	a     : Optional[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
	]

	a     : 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
	]

	a     : Union[str, Any]	  =					[
	    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
	]
	a     : List[Any]	  =					[
	    4_1_6, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
	    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
	    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
	    4_1_6, 4_1_6, 4_1_6, 4_1_6
	]

	a     : int	  =					[
	    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
	]
	a     : Tuple	  =					[
	    8_5_4, 8_4_2, 1_3_5_3, 1_1_4, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 1_1_4, 5_9_1, 8_4_2,
	    1_8_5, 4_1_7, 1_8_5, 8_4_2, 3_0_7, 8_4_2, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 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
	]

	a     : str	  =					[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]

	a     : List[Any]	  =					[
	    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
	]
	a     : Dict	  =					[
	    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
	]
	a     : List[str]	  =					[
	    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
	]
	a     : Optional[Any]	  =					[
	    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
	]
	a     : Dict	  =					[
	    -0.0_492, -0.0_524, -0.0_565, -0.0_640, -0.0_686, -0.0_684, -0.0_677, -0.0_664,
	    -0.0_605, -0.0_490, -0.0_330, -0.0_168, -0.0_083, -0.0_075, -0.0_051, 0.0_025,
	    0.0_136, 0.0_261, 0.0_386, 0.0_497, 0.0_580, 0.0_599, 0.0_583, 0.0_614,
	    0.0_740, 0.0_889, 0.1_023, 0.1_162, 0.1_211, 0.1_212, 0.1_251, 0.1_336,
	    0.1_502, 0.1_686, 0.1_883, 0.2_148, 0.2_363, 0.2_458, 0.2_507, 0.2_531
	]
	a     : Union[str, Any]	  =					[
	    0.0_256, -0.0_544, 0.1_600, -0.0_032, 0.1_066, 0.0_825, -0.0_013, 0.3_440,
	    0.0_210, 0.0_412, -0.1_777, -0.0_892, -0.0_164, 0.0_285, -0.0_613, -0.0_617,
	    -0.0_137, -0.0_201, -0.0_175, 0.0_215, -0.0_627, 0.0_520, -0.0_730, 0.0_970,
	    -0.0_100, 0.0_442, -0.0_586, 0.0_207, -0.0_015, -0.0_082
	]
	a     : Optional[int]	  =					[
	    0.0_415, 0.0_877, 0.0_022, -0.0_055, 0.0_751, 0.0_334, 0.0_324, -0.0_068,
	    0.0_011, 0.0_017, -0.0_676, 0.0_655, -0.0_143, 0.0_399, 0.0_303, 0.0_743,
	    -0.0_168, -0.0_394, -0.1_113, 0.0_124, 0.0_442, 0.0_267, -0.0_003, -0.1_536,
	    -0.0_116, -0.1_837, -0.0_180, -0.1_026, -0.0_777, -0.0_456
	]
	a     : List[Any]	  =					[
	    7_6, 2_7, 4_0, 3_0, 7_6, 4_6, 4_4, 4_7, 4_0, 3_7, 3_8, 3_1, 4_5, 4_5, 7_6, 3_8, 3_1, 3_3,
	    4_5, 7_6, 4_1, 3_2, 7_6, 4_5, 4_6, 4_1, 4_0, 3_1, 7_8, 7_6
	]

	def    __a     (					self    ) ->      Dict:





								"""simple docstring"""


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

	@slow
	def    __a     (					self    ) ->      List[str]:





								"""simple docstring"""


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

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

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

								set_seed(0    )
								lowercase__  :	Optional[int]        =							model._sample(lowerCamelCase	,				lowerCamelCase	,				[2]	,				sample_length=40 * model.priors[2].raw_to_tokens	,				save_results=lowerCamelCase    )
								self.assertIn(zs[2][0].detach().cpu().tolist()	,				[self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]    )

	@slow
	def    __a     (					self    ) ->      Optional[Any]:





								"""simple docstring"""


								lowercase__  :	Tuple        =							False
								lowercase__  :	Tuple        =							JukeboxModel.from_pretrained(self.model_id	,				min_duration=0    ).eval()

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

								lowercase__  :	Union[str, Any]        =							model.priors[0]
								lowercase__  :	Tuple        =							0
								lowercase__  :	Tuple        =							top_prior.get_music_tokens_conds(lowerCamelCase	,				start=lowerCamelCase	,				end=start + top_prior.n_ctx    )
								lowercase__  :	Optional[Any]        =							top_prior.get_metadata(labels[0].clone()	,				lowerCamelCase	,				1058304	,				0    )

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

								lowercase__ ,       lowercase__ ,       lowercase__  :	Tuple        =							top_prior.get_cond(lowerCamelCase	,				lowerCamelCase    )
								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    ) ->      int:





								"""simple docstring"""


								lowercase__  :	int        =							False

								lowercase__  :	Dict        =							JukeboxModel.from_pretrained(self.model_id	,				min_duration=0    ).eval()
								set_seed(0    )
								lowercase__  :	Optional[int]        =							torch.rand((1, 5120, 1)    )
								lowercase__  :	Tuple        =							list(self.prepare_inputs()    )

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

								lowercase__  :	str        =							torch.cat((zs[0], torch.zeros(1	,				2048 - zs[0].shape[-1]    ))	,				dim=-1    ).long()
								lowercase__  :	List[Any]        =							[upper_a, model.vqvae.encode(lowerCamelCase	,				start_level=1	,				bs_chunks=waveform.shape[0]    )[0], None]
								lowercase__  :	Union[str, Any]        =							model._sample(
								    lowerCamelCase	,				lowerCamelCase	,				sample_levels=[1]	,				save_results=lowerCamelCase	,				sample_length=40 * model.priors[1].raw_to_tokens    )
								torch.testing.assert_allclose(zs[1][0][:40]	,				torch.tensor(self.EXPECTED_PRIMED_1    )    )

								lowercase__  :	Any        =							torch.cat((zs[1], torch.zeros(1	,				2048 - zs[1].shape[-1]    ))	,				dim=-1    ).long()
								lowercase__  :	Union[str, Any]        =							[upper_a, upper_a, model.vqvae.encode(lowerCamelCase	,				start_level=0	,				bs_chunks=waveform.shape[0]    )[0]]
								lowercase__  :	Tuple        =							model._sample(
								    lowerCamelCase	,				lowerCamelCase	,				sample_levels=[2]	,				save_results=lowerCamelCase	,				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    ) ->      str:





								"""simple docstring"""


								lowercase__  :	Optional[int]        =							JukeboxModel.from_pretrained(self.model_id	,				min_duration=0    ).eval()
								set_seed(0    )
								lowercase__  :	str        =							torch.rand((1, 5120, 1)    )
								with torch.no_grad():
															lowercase__  :	List[Any]        =							model.vqvae.encode(lowerCamelCase	,				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():
															lowercase__  :	List[str]        =							model.vqvae.decode(lowerCamelCase	,				start_level=2	,				bs_chunks=x.shape[0]    )
								torch.testing.assert_allclose(x[0, :40, 0]	,				torch.tensor(self.EXPECTED_VQVAE_DECODE    )	,				atol=1E-4	,				rtol=1E-4    )







@require_torch
class 				UpperCAmelCase(					unittest.TestCase					):



	"""simple docstring"""






	a     : Union[str, Any]	  =					(JukeboxModel,) if is_torch_available() else ()
	a     : str	  =					"""openai/jukebox-5b-lyrics"""
	a     : Tuple	  =					{
	    """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
	a     : int	  =					[
	    1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
	    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
	    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
	    6_5_3, 6_5_3, 6_5_3, 6_5_3, 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
	]
	a     : Optional[int]	  =					[
	    1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
	    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
	    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
	    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
	    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3
	]

	a     : 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
	]
	a     : Optional[int]	  =					[
	    4_1_6, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
	    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
	    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
	    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
	    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
	]

	a     : int	  =					[
	    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
	]
	a     : List[str]	  =					[
	    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
	]

	a     : 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
	]
	a     : 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, 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
	]

	a     : int	  =					[
	    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
	]
	a     : int	  =					[
	    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    ) ->      str:





								"""simple docstring"""


								lowercase__  :	Optional[Any]        =							JukeboxTokenizer.from_pretrained(lowerCamelCase    )
								lowercase__  :	Tuple        =							tokenizer(**self.metas    )["input_ids"]
								return tokens

	@slow
	def    __a     (					self    ) ->      Optional[int]:





								"""simple docstring"""


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

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

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

								set_seed(0    )
								lowercase__  :	Optional[Any]        =							model._sample(lowerCamelCase	,				lowerCamelCase	,				[2]	,				sample_length=60 * model.priors[2].raw_to_tokens	,				save_results=lowerCamelCase    )
								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    ) ->      Optional[Any]:





								"""simple docstring"""


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

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







	@slow
	def    __a     (					self    ) ->      str:





								"""simple docstring"""


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

								lowercase__  :	List[Any]        =							self.prepare_inputs(lowerCamelCase    )[0].cuda()
								lowercase__  :	List[Any]        =							model.get_metadata(lowerCamelCase	,				0	,				7680	,				0    )
								set_seed(0    )
								lowercase__  :	Any        =							model.sample(1	,				metadata=lowerCamelCase	,				sample_tokens=60    )
								self.assertIn(outputs[0].cpu().tolist()	,				[self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]    )





import numpy as np
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			int:
							lowercase__  :	str        =							n_draws * class_counts / class_counts.sum()
							# floored means we don't overshoot n_samples, but probably undershoot
							lowercase__  :	Union[str, Any]        =							np.floor(SCREAMING_SNAKE_CASE_    )
							# we add samples according to how much "left over" probability
							# they had, until we arrive at n_samples
							lowercase__  :	Tuple        =							int(n_draws - floored.sum()    )
							if need_to_add > 0:
														lowercase__  :	Union[str, Any]        =							continuous - floored
														lowercase__  :	Optional[Any]        =							np.sort(np.unique(SCREAMING_SNAKE_CASE_    )    )[::-1]
														# add according to remainder, but break ties
														# randomly to avoid biases
														for value in values:
																					((lowercase__) ,       )  :	Optional[Any]        =							np.where(remainder == value    )
																					# if we need_to_add less than what's in inds
																					# we draw randomly from them.
																					# if we need to add more, we add them all and
																					# go to the next value
																					lowercase__  :	Dict        =							min(len(SCREAMING_SNAKE_CASE_    )    ,SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	Any        =							rng.choice(SCREAMING_SNAKE_CASE_    ,size=SCREAMING_SNAKE_CASE_    ,replace=SCREAMING_SNAKE_CASE_    )
																					floored[inds] += 1
																					need_to_add -= add_now
																					if need_to_add == 0:
																												break
							return floored.astype(np.intaa    )




def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=10    )      ->			List[str]:
							lowercase__ ,       lowercase__  :	int        =							np.unique(SCREAMING_SNAKE_CASE_    ,return_inverse=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	int        =							classes.shape[0]
							lowercase__  :	Union[str, Any]        =							np.bincount(SCREAMING_SNAKE_CASE_    )
							if np.min(SCREAMING_SNAKE_CASE_    ) < 2:
														raise ValueError("Minimum class count error"    )
							if n_train < n_classes:
														raise ValueError(
														    "The train_size = %d should be greater or " "equal to the number of classes = %d" % (n_train, n_classes)    )
							if n_test < n_classes:
														raise ValueError(
														    "The test_size = %d should be greater or " "equal to the number of classes = %d" % (n_test, n_classes)    )
							lowercase__  :	List[Any]        =							np.split(np.argsort(SCREAMING_SNAKE_CASE_    ,kind="mergesort"    )    ,np.cumsum(SCREAMING_SNAKE_CASE_    )[:-1]    )
							for _ in range(SCREAMING_SNAKE_CASE_    ):
														lowercase__  :	str        =							approximate_mode(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
														lowercase__  :	int        =							class_counts - n_i
														lowercase__  :	int        =							approximate_mode(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )

														lowercase__  :	Tuple        =							[]
														lowercase__  :	int        =							[]

														for i in range(SCREAMING_SNAKE_CASE_    ):
																					lowercase__  :	Union[str, Any]        =							rng.permutation(class_counts[i]    )
																					lowercase__  :	List[Any]        =							class_indices[i].take(SCREAMING_SNAKE_CASE_    ,mode="clip"    )
																					train.extend(perm_indices_class_i[: n_i[i]]    )
																					test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]    )
														lowercase__  :	Tuple        =							rng.permutation(SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Dict        =							rng.permutation(SCREAMING_SNAKE_CASE_    )

														yield train, test