
import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					lowerCAmelCase						:   List[Any]							=       Dataset.from_dict({'''i''': range(17					)}					)
					lowerCAmelCase						:   Dict							=       len(snake_case__					)
					lowerCAmelCase						:   str							=       3
					lowerCAmelCase						:   Optional[Any]							=       [
					    split_dataset_by_node(snake_case__							,					rank=snake_case__							,					world_size=snake_case__					) for rank in range(snake_case__					)
					]
					assert sum(len(snake_case__					) for ds in datasets_per_rank					) == full_size
					assert len({tuple(x.values()					) for ds in datasets_per_rank for x in ds}					) == full_size
def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					def gen():
										return ({"i": i} for i in range(17					))

					lowerCAmelCase						:   Dict							=       3
					lowerCAmelCase						:   Union[str, Any]							=       IterableDataset.from_generator(snake_case__					)
					lowerCAmelCase						:   Optional[int]							=       len(list(snake_case__					)					)
					lowerCAmelCase						:   Optional[Any]							=       [
					    split_dataset_by_node(snake_case__							,					rank=snake_case__							,					world_size=snake_case__					) for rank in range(snake_case__					)
					]
					assert sum(len(list(snake_case__					)					) for ds in datasets_per_rank					) == full_size
					assert len({tuple(x.values()					) for ds in datasets_per_rank for x in ds}					) == full_size
@pytest.mark.parametrize('''shards_per_node'''							,					[1, 2, 3]					)
def       UpperCAmelCase__							(    __magic_name__  :       List[str]					):


					'''simple docstring'''

					def gen(__magic_name__  :       Optional[int]					):
										for shard in shards:
															yield from ({"i": i, "shard": shard} for i in range(17					))

					lowerCAmelCase						:   Any							=       3
					lowerCAmelCase						:   List[str]							=       shards_per_node * world_size
					lowerCAmelCase						:   List[Any]							=       {'''shards''': [f'''shard_{shard_idx}.txt''' for shard_idx in range(snake_case__					)]}
					lowerCAmelCase						:   Tuple							=       IterableDataset.from_generator(snake_case__							,					gen_kwargs=snake_case__					)
					lowerCAmelCase						:   Any							=       len(list(snake_case__					)					)
					assert full_ds.n_shards == world_size * shards_per_node
					lowerCAmelCase						:   str							=       [
					    split_dataset_by_node(snake_case__							,					rank=snake_case__							,					world_size=snake_case__					) for rank in range(snake_case__					)
					]
					assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
					assert sum(len(list(snake_case__					)					) for ds in datasets_per_rank					) == full_size
					assert len({tuple(x.values()					) for ds in datasets_per_rank for x in ds}					) == full_size
def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					def gen():
										return ({"i": i} for i in range(17					))

					lowerCAmelCase						:   List[Any]							=       2
					lowerCAmelCase						:   Tuple							=       IterableDataset.from_generator(snake_case__					)
					lowerCAmelCase						:   Dict							=       len(list(snake_case__					)					)

					lowerCAmelCase						:   List[Any]							=       split_dataset_by_node(snake_case__							,					rank=0							,					world_size=snake_case__					).shuffle(seed=42					)
					assert len(list(snake_case__					)					) == 1 + full_size // world_size
					with pytest.raises(snake_case__					):
										split_dataset_by_node(snake_case__							,					rank=0							,					world_size=snake_case__					).shuffle()

					lowerCAmelCase						:   List[str]							=       split_dataset_by_node(full_ds.shuffle(seed=42					)							,					rank=0							,					world_size=snake_case__					)
					assert len(list(snake_case__					)					) == 1 + full_size // world_size
					with pytest.raises(snake_case__					):
										split_dataset_by_node(full_ds.shuffle()							,					rank=0							,					world_size=snake_case__					)
@pytest.mark.parametrize('''streaming'''							,					[False, True]					)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''							,					reason='''execute_subprocess_async doesn\'t support windows'''					)
@pytest.mark.integration
def       UpperCAmelCase__							(    __magic_name__  :       List[Any]					):


					'''simple docstring'''

					lowerCAmelCase						:   List[str]							=       2
					lowerCAmelCase						:   List[str]							=       get_torch_dist_unique_port()
					lowerCAmelCase						:   Dict							=       Path(snake_case__					).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
					lowerCAmelCase						:   Union[str, Any]							=       f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
					lowerCAmelCase						:   List[str]							=       f'''
        --streaming={streaming}
    '''.split()
					lowerCAmelCase						:   Union[str, Any]							=       [sys.executable] + distributed_args + args
					execute_subprocess_async(snake_case__							,					env=os.environ.copy()					)







@pytest.mark.parametrize(
    '''nproc_per_node, num_workers'''							,					[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]							,					)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''							,					reason='''execute_subprocess_async doesn\'t support windows'''					)
@pytest.mark.integration
def       UpperCAmelCase__							(    __magic_name__  :       Optional[Any]							,					__magic_name__  :       Tuple					):


					'''simple docstring'''

					lowerCAmelCase						:   Union[str, Any]							=       True
					lowerCAmelCase						:   int							=       get_torch_dist_unique_port()
					lowerCAmelCase						:   Optional[Any]							=       Path(snake_case__					).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
					lowerCAmelCase						:   List[Any]							=       f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
					lowerCAmelCase						:   Optional[int]							=       f'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
					lowerCAmelCase						:   Any							=       [sys.executable] + distributed_args + args
					execute_subprocess_async(snake_case__							,					env=os.environ.copy()					)


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 						__magic_name__				(  unittest.TestCase      ):
		_lowerCAmelCase						=		(JukeboxModel,) if is_torch_available() else ()
		_lowerCAmelCase						=		"openai/jukebox-1b-lyrics"
		_lowerCAmelCase						=		{
		    "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
		_lowerCAmelCase						=		[
		    1_864, 1_536, 1_213, 1_870, 1_357, 1_536, 519, 880, 1_323, 789, 1_082, 534,
		    1_000, 1_445, 1_105, 1_130, 967, 515, 1_434, 1_620, 534, 1_495, 283, 1_445,
		    333, 1_307, 539, 1_631, 1_528, 375, 1_434, 673, 627, 710, 778, 1_883,
		    1_405, 1_276, 1_455, 1_228
		]

		_lowerCAmelCase						=		[
		    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653
		]

		_lowerCAmelCase						=		[
		    1_125, 1_751, 697, 1_776, 1_141, 1_476, 391, 697, 1_125, 684, 867, 416,
		    844, 1_372, 1_274, 717, 1_274, 844, 1_299, 1_419, 697, 1_370, 317, 1_125,
		    191, 1_440, 1_370, 1_440, 1_370, 282, 1_621, 1_370, 368, 349, 867, 1_872,
		    1_262, 869, 1_728, 747
		]
		_lowerCAmelCase						=		[
		    416, 416, 1_125, 1_125, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416
		]

		_lowerCAmelCase						=		[
		    1_755, 842, 307, 1_843, 1_022, 1_395, 234, 1_554, 806, 739, 1_022, 442,
		    616, 556, 268, 1_499, 933, 457, 1_440, 1_837, 755, 985, 308, 902,
		    293, 1_443, 1_671, 1_141, 1_533, 555, 1_562, 1_061, 287, 417, 1_022, 2_008,
		    1_186, 1_015, 1_777, 268
		]
		_lowerCAmelCase						=		[
		    854, 842, 1_353, 114, 1_353, 842, 185, 842, 185, 114, 591, 842,
		    185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842,
		    591, 842, 1_353, 842, 185, 842, 591, 842, 591, 114, 591, 842,
		    185, 842, 591, 89
		]

		_lowerCAmelCase						=		[1_058_304, 0, 786_432, 7_169, 507, 76, 27, 40, 30, 76]

		_lowerCAmelCase						=		[
		    390, 1_160, 1_002, 1_907, 1_788, 1_788, 1_788, 1_907, 1_002, 1_002, 1_854, 1_002,
		    1_002, 1_002, 1_002, 1_002, 1_002, 1_160, 1_160, 1_606, 596, 596, 1_160, 1_002,
		    1_516, 596, 1_002, 1_002, 1_002, 1_907, 1_788, 1_788, 1_788, 1_854, 1_788, 1_907,
		    1_907, 1_788, 596, 1_626
		]
		_lowerCAmelCase						=		[
		    1_236, 1_668, 1_484, 1_920, 1_848, 1_409, 139, 864, 1_828, 1_272, 1_599, 824,
		    1_672, 139, 555, 1_484, 824, 1_920, 555, 596, 1_579, 1_599, 1_231, 1_599,
		    1_637, 1_407, 212, 824, 1_599, 116, 1_433, 824, 258, 1_599, 1_433, 1_895,
		    1_063, 1_433, 1_433, 1_599
		]
		_lowerCAmelCase						=		[
		    1_684, 1_873, 1_119, 1_189, 395, 611, 1_901, 972, 890, 1_337, 1_392, 1_927,
		    96, 972, 672, 780, 1_119, 890, 158, 771, 1_073, 1_927, 353, 1_331,
		    1_269, 1_459, 1_333, 1_645, 812, 1_577, 1_337, 606, 353, 981, 1_466, 619,
		    197, 391, 302, 1_930
		]
		_lowerCAmelCase						=		[
		    390, 1_160, 1_002, 1_907, 1_788, 1_788, 1_788, 1_907, 1_002, 1_002, 1_854, 1_002,
		    1_002, 1_002, 1_002, 1_002, 1_002, 1_160, 1_160, 1_606, 596, 596, 1_160, 1_002,
		    1_516, 596, 1_002, 1_002, 1_002, 1_907, 1_788, 1_788, 1_788, 1_854, 1_788, 1_907,
		    1_907, 1_788, 596, 1_626
		]
		_lowerCAmelCase						=		[
		    -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
		]
		_lowerCAmelCase						=		[
		    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
		]
		_lowerCAmelCase						=		[
		    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
		]
		_lowerCAmelCase						=		[
		    76, 27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45, 45, 76, 38, 31, 33,
		    45, 76, 41, 32, 76, 45, 46, 41, 40, 31, 78, 76
		]





		def  _A							(							self      :		Dict			):
							lowerCAmelCase						:   int							=       JukeboxTokenizer.from_pretrained(self.model_id			)
							lowerCAmelCase						:   Union[str, Any]							=       tokenizer(**self.metas			)['''input_ids''']
							return tokens





		@slow
		def  _A							(							self      :		Optional[int]			):
							lowerCAmelCase						:   Any							=       JukeboxModel.from_pretrained(self.model_id  ,					min_duration=0			).eval()
							lowerCAmelCase						:   Union[str, Any]							=       self.prepare_inputs()

							set_seed(0			)
							lowerCAmelCase						:   Optional[int]							=       [torch.zeros(1  ,					0  ,					dtype=torch.long			).cpu() for _ in range(3			)]
							lowerCAmelCase						:   Optional[Any]							=       model._sample(lowerCamelCase__  ,					lowerCamelCase__  ,					[0]  ,					sample_length=4_0 * 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			)
							lowerCAmelCase						:   Tuple							=       model._sample(lowerCamelCase__  ,					lowerCamelCase__  ,					[1]  ,					sample_length=4_0 * 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			)
							lowerCAmelCase						:   Tuple							=       model._sample(lowerCamelCase__  ,					lowerCamelCase__  ,					[2]  ,					sample_length=4_0 * 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      :		str			):
							lowerCAmelCase						:   str							=       False
							lowerCAmelCase						:   Any							=       JukeboxModel.from_pretrained(self.model_id  ,					min_duration=0			).eval()

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

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

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

							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase						:   Dict							=       top_prior.get_cond(lowerCamelCase__  ,					lowerCamelCase__			)
							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  _A							(							self      :		List[str]			):
							lowerCAmelCase						:   Tuple							=       False

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

							lowerCAmelCase						:   List[str]							=       [model.vqvae.encode(lowerCamelCase__  ,					start_level=2  ,					bs_chunks=waveform.shape[0]			)[0], None, None]
							lowerCAmelCase						:   List[Any]							=       model._sample(
							    lowerCamelCase__  ,					lowerCamelCase__  ,					sample_levels=[0]  ,					save_results=lowerCamelCase__  ,					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			)			)

							lowerCAmelCase						:   List[Any]							=       torch.cat((zs[0], torch.zeros(1  ,					2_0_4_8 - zs[0].shape[-1]			))  ,					dim=-1			).long()
							lowerCAmelCase						:   List[str]							=       [upper_a, model.vqvae.encode(lowerCamelCase__  ,					start_level=1  ,					bs_chunks=waveform.shape[0]			)[0], None]
							lowerCAmelCase						:   List[Any]							=       model._sample(
							    lowerCamelCase__  ,					lowerCamelCase__  ,					sample_levels=[1]  ,					save_results=lowerCamelCase__  ,					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			)			)

							lowerCAmelCase						:   Any							=       torch.cat((zs[1], torch.zeros(1  ,					2_0_4_8 - zs[1].shape[-1]			))  ,					dim=-1			).long()
							lowerCAmelCase						:   str							=       [upper_a, upper_a, model.vqvae.encode(lowerCamelCase__  ,					start_level=0  ,					bs_chunks=waveform.shape[0]			)[0]]
							lowerCAmelCase						:   List[Any]							=       model._sample(
							    lowerCamelCase__  ,					lowerCamelCase__  ,					sample_levels=[2]  ,					save_results=lowerCamelCase__  ,					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  _A							(							self      :		Any			):
							lowerCAmelCase						:   Any							=       JukeboxModel.from_pretrained(self.model_id  ,					min_duration=0			).eval()
							set_seed(0			)
							lowerCAmelCase						:   Tuple							=       torch.rand((1, 5_1_2_0, 1)			)
							with torch.no_grad():
												lowerCAmelCase						:   Tuple							=       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():
												lowerCAmelCase						:   int							=       model.vqvae.decode(lowerCamelCase__  ,					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 						__magic_name__				(  unittest.TestCase      ):
		_lowerCAmelCase						=		(JukeboxModel,) if is_torch_available() else ()
		_lowerCAmelCase						=		"openai/jukebox-5b-lyrics"
		_lowerCAmelCase						=		{
		    "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
		_lowerCAmelCase						=		[
		    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    1_489, 1_489, 1_489, 1_489, 1_150, 1_853, 1_509, 1_150, 1_357, 1_509, 6, 1_272
		]
		_lowerCAmelCase						=		[
		    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
		]

		_lowerCAmelCase						=		[
		    1_125, 416, 1_125, 1_125, 1_125, 1_125, 1_125, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
		]
		_lowerCAmelCase						=		[
		    416, 416, 1_125, 1_125, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416
		]

		_lowerCAmelCase						=		[
		    1_755, 1_061, 234, 1_755, 1_061, 1_755, 185, 290, 307, 307, 616, 616,
		    616, 616, 616, 616, 307, 290, 417, 1_755, 234, 1_755, 185, 290,
		    290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1_755,
		    234, 234, 1_755, 234, 185, 185, 307, 616, 616, 616, 616, 290,
		    1_755, 1_755, 1_755, 234, 234, 1_755, 1_572, 290, 307, 616, 34, 616
		]
		_lowerCAmelCase						=		[
		    854, 842, 1_353, 114, 1_353, 842, 185, 842, 185, 114, 591, 842, 185,
		    417, 185, 842, 307, 842, 591, 842, 185, 842, 185, 842, 591, 842,
		    1_353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591,
		    89, 591, 842, 591, 842, 591, 417, 1_372, 842, 1_372, 842, 34, 842,
		    185, 89, 591, 842, 185, 842, 591, 632
		]

		_lowerCAmelCase						=		[
		    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
		]
		_lowerCAmelCase						=		[
		    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 1_853, 1_177, 1_536, 1_228,
		    710, 475, 1_489, 1_229, 1_224, 231, 1_224, 252, 1_434, 653, 475,
		    1_106, 1_877, 1_599, 1_228, 1_600, 1_683, 1_182, 1_853, 475, 1_864,
		    252, 1_229, 1_434, 2_001
		]

		_lowerCAmelCase						=		[
		    1_125, 1_125, 416, 1_125, 1_125, 416, 1_125, 1_125, 416, 416, 1_125, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
		]
		_lowerCAmelCase						=		[
		    491, 1_755, 34, 1_613, 1_755, 417, 992, 1_613, 222, 842, 1_353, 1_613,
		    844, 632, 185, 1_613, 844, 632, 185, 1_613, 185, 842, 677, 1_613,
		    185, 114, 1_353, 1_613, 307, 89, 844, 1_613, 307, 1_332, 234, 1_979,
		    307, 89, 1_353, 616, 34, 842, 185, 842, 34, 842, 185, 842,
		    307, 114, 185, 89, 34, 1_268, 185, 89, 34, 842, 185, 89
		]





		def  _A							(							self      :		List[Any]  ,					lowerCamelCase__      :		int			):
							lowerCAmelCase						:   Any							=       JukeboxTokenizer.from_pretrained(lowerCamelCase__			)
							lowerCAmelCase						:   List[Any]							=       tokenizer(**self.metas			)['''input_ids''']
							return tokens





		@slow
		def  _A							(							self      :		int			):
							lowerCAmelCase						:   Any							=       JukeboxModel.from_pretrained(self.model_id  ,					min_duration=0			).eval()
							lowerCAmelCase						:   Dict							=       self.prepare_inputs(self.model_id			)

							set_seed(0			)
							lowerCAmelCase						:   List[str]							=       [torch.zeros(1  ,					0  ,					dtype=torch.long			).cpu() for _ in range(3			)]
							lowerCAmelCase						:   Tuple							=       model._sample(lowerCamelCase__  ,					lowerCamelCase__  ,					[0]  ,					sample_length=6_0 * 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			)
							lowerCAmelCase						:   int							=       model._sample(lowerCamelCase__  ,					lowerCamelCase__  ,					[1]  ,					sample_length=6_0 * 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			)
							lowerCAmelCase						:   Dict							=       model._sample(lowerCamelCase__  ,					lowerCamelCase__  ,					[2]  ,					sample_length=6_0 * 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      :		str			):
							lowerCAmelCase						:   int							=       JukeboxModel.from_pretrained(self.model_id  ,					min_duration=0			).eval()
							lowerCAmelCase						:   Optional[int]							=       [i.cuda() for i in self.prepare_inputs(self.model_id			)]

							set_seed(0			)
							model.priors[0].cuda()
							lowerCAmelCase						:   List[str]							=       [torch.zeros(1  ,					0  ,					dtype=torch.long			).cuda() for _ in range(3			)]
							lowerCAmelCase						:   int							=       model._sample(lowerCamelCase__  ,					lowerCamelCase__  ,					[0]  ,					sample_length=6_0 * 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()
							lowerCAmelCase						:   int							=       model._sample(lowerCamelCase__  ,					lowerCamelCase__  ,					[1]  ,					sample_length=6_0 * 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()
							lowerCAmelCase						:   int							=       model._sample(lowerCamelCase__  ,					lowerCamelCase__  ,					[2]  ,					sample_length=6_0 * 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      :		Tuple			):
							lowerCAmelCase						:   Dict							=       '''ArthurZ/jukebox_prior_0'''
							lowerCAmelCase						:   Tuple							=       JukeboxPrior.from_pretrained(lowerCamelCase__  ,					min_duration=0			).eval().half().to('''cuda'''			)

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