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

import datasets


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

__SCREAMING_SNAKE_CASE     :				Tuple             =       '\\nFrugalScore 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.\n'


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


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





		def  _A							(							self      :		Dict			):
							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  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		Union[str, Any]			):
							if self.config_name == "default":
												lowerCAmelCase						:   Any							=       '''moussaKam/frugalscore_tiny_bert-base_bert-score'''
							else:
												lowerCAmelCase						:   Any							=       self.config_name
							lowerCAmelCase						:   Optional[Any]							=       AutoModelForSequenceClassification.from_pretrained(lowerCamelCase__			)
							lowerCAmelCase						:   Tuple							=       AutoTokenizer.from_pretrained(lowerCamelCase__			)





		def  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		Tuple=3_2  ,					lowerCamelCase__      :		List[Any]=1_2_8  ,					lowerCamelCase__      :		str=None  ,					):
							assert len(lowerCamelCase__			) == len(
							    lowerCamelCase__			), "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:
												lowerCAmelCase						:   str							=       '''gpu''' if torch.cuda.is_available() else '''cpu'''
							lowerCAmelCase						:   str							=       TrainingArguments(
							    '''trainer'''  ,					fpaa=(device == '''gpu''')  ,					per_device_eval_batch_size=lowerCamelCase__  ,					report_to='''all'''  ,					no_cuda=(device == '''cpu''')  ,					log_level='''warning'''  ,					)
							lowerCAmelCase						:   str							=       {'''sentence1''': predictions, '''sentence2''': references}
							lowerCAmelCase						:   str							=       datasets.Dataset.from_dict(lowerCamelCase__			)

							def tokenize_function(lowerCamelCase__      :		str			):
												return self.tokenizer(
												    data['''sentence1''']  ,					data['''sentence2''']  ,					max_length=lowerCamelCase__  ,					truncation=lowerCamelCase__  ,					padding=lowerCamelCase__			)

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


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]			)
