

'''simple docstring'''







import unittest
from unittest import skip

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


if is_torch_available():
							import torch

							from transformers import JukeboxModel, JukeboxPrior, JukeboxTokenizer
@require_torch
class 					__a   (		unittest.TestCase  ):
						__SCREAMING_SNAKE_CASE     :					Tuple  =      (JukeboxModel,) if is_torch_available() else ()
						__SCREAMING_SNAKE_CASE     :					Optional[Any]  =      'openai/jukebox-1b-lyrics'
						__SCREAMING_SNAKE_CASE     :					Any  =      {
						    'artist': 'Zac Brown Band',
						    'genres': 'Country',
						    'lyrics': 'I met a traveller from an antique land,\n    Who said "Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ',
						}
						# fmt: off
						__SCREAMING_SNAKE_CASE     :					str  =      [
						    1_8_6_4, 1_5_3_6, 1_2_1_3, 1_8_7_0, 1_3_5_7, 1_5_3_6, 5_1_9, 8_8_0, 1_3_2_3, 7_8_9, 1_0_8_2, 5_3_4,
						    1_0_0_0, 1_4_4_5, 1_1_0_5, 1_1_3_0, 9_6_7, 5_1_5, 1_4_3_4, 1_6_2_0, 5_3_4, 1_4_9_5, 2_8_3, 1_4_4_5,
						    3_3_3, 1_3_0_7, 5_3_9, 1_6_3_1, 1_5_2_8, 3_7_5, 1_4_3_4, 6_7_3, 6_2_7, 7_1_0, 7_7_8, 1_8_8_3,
						    1_4_0_5, 1_2_7_6, 1_4_5_5, 1_2_2_8
						]

						__SCREAMING_SNAKE_CASE     :					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
						]

						__SCREAMING_SNAKE_CASE     :					str  =      [
						    1_1_2_5, 1_7_5_1, 6_9_7, 1_7_7_6, 1_1_4_1, 1_4_7_6, 3_9_1, 6_9_7, 1_1_2_5, 6_8_4, 8_6_7, 4_1_6,
						    8_4_4, 1_3_7_2, 1_2_7_4, 7_1_7, 1_2_7_4, 8_4_4, 1_2_9_9, 1_4_1_9, 6_9_7, 1_3_7_0, 3_1_7, 1_1_2_5,
						    1_9_1, 1_4_4_0, 1_3_7_0, 1_4_4_0, 1_3_7_0, 2_8_2, 1_6_2_1, 1_3_7_0, 3_6_8, 3_4_9, 8_6_7, 1_8_7_2,
						    1_2_6_2, 8_6_9, 1_7_2_8, 7_4_7
						]
						__SCREAMING_SNAKE_CASE     :					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
						]

						__SCREAMING_SNAKE_CASE     :					str  =      [
						    1_7_5_5, 8_4_2, 3_0_7, 1_8_4_3, 1_0_2_2, 1_3_9_5, 2_3_4, 1_5_5_4, 8_0_6, 7_3_9, 1_0_2_2, 4_4_2,
						    6_1_6, 5_5_6, 2_6_8, 1_4_9_9, 9_3_3, 4_5_7, 1_4_4_0, 1_8_3_7, 7_5_5, 9_8_5, 3_0_8, 9_0_2,
						    2_9_3, 1_4_4_3, 1_6_7_1, 1_1_4_1, 1_5_3_3, 5_5_5, 1_5_6_2, 1_0_6_1, 2_8_7, 4_1_7, 1_0_2_2, 2_0_0_8,
						    1_1_8_6, 1_0_1_5, 1_7_7_7, 2_6_8
						]
						__SCREAMING_SNAKE_CASE     :					List[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
						]

						__SCREAMING_SNAKE_CASE     :					Tuple  =      [1_0_5_8_3_0_4, 0, 7_8_6_4_3_2, 7_1_6_9, 5_0_7, 7_6, 2_7, 4_0, 3_0, 7_6]

						__SCREAMING_SNAKE_CASE     :					Optional[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
						]
						__SCREAMING_SNAKE_CASE     :					Optional[Any]  =      [
						    1_2_3_6, 1_6_6_8, 1_4_8_4, 1_9_2_0, 1_8_4_8, 1_4_0_9, 1_3_9, 8_6_4, 1_8_2_8, 1_2_7_2, 1_5_9_9, 8_2_4,
						    1_6_7_2, 1_3_9, 5_5_5, 1_4_8_4, 8_2_4, 1_9_2_0, 5_5_5, 5_9_6, 1_5_7_9, 1_5_9_9, 1_2_3_1, 1_5_9_9,
						    1_6_3_7, 1_4_0_7, 2_1_2, 8_2_4, 1_5_9_9, 1_1_6, 1_4_3_3, 8_2_4, 2_5_8, 1_5_9_9, 1_4_3_3, 1_8_9_5,
						    1_0_6_3, 1_4_3_3, 1_4_3_3, 1_5_9_9
						]
						__SCREAMING_SNAKE_CASE     :					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
						]
						__SCREAMING_SNAKE_CASE     :					Tuple  =      [
						    3_9_0, 1_1_6_0, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_9_0_7, 1_0_0_2, 1_0_0_2, 1_8_5_4, 1_0_0_2,
						    1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_1_6_0, 1_1_6_0, 1_6_0_6, 5_9_6, 5_9_6, 1_1_6_0, 1_0_0_2,
						    1_5_1_6, 5_9_6, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_8_5_4, 1_7_8_8, 1_9_0_7,
						    1_9_0_7, 1_7_8_8, 5_9_6, 1_6_2_6
						]
						__SCREAMING_SNAKE_CASE     :					str  =      [
						    -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
						]
						__SCREAMING_SNAKE_CASE     :					Tuple  =      [
						    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
						]
						__SCREAMING_SNAKE_CASE     :					Optional[Any]  =      [
						    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
						]
						__SCREAMING_SNAKE_CASE     :					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 	_UpperCAmelCase							(		self	:						Union[str, Any])						->Optional[Any]:

							"""simple docstring"""



							_lowercase							=     JukeboxTokenizer.from_pretrained(self.model_id)
							_lowercase							=     tokenizer(**self.metas)["""input_ids"""]
							return tokens

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

							"""simple docstring"""



							_lowercase							=     JukeboxModel.from_pretrained(self.model_id    ,			min_duration=0).eval()
							_lowercase							=     self.prepare_inputs()

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

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

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

						@slow
						def 	_UpperCAmelCase							(		self	:						List[str])						->Optional[int]:

							"""simple docstring"""



							_lowercase							=     False
							_lowercase							=     JukeboxModel.from_pretrained(self.model_id    ,			min_duration=0).eval()

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

							_lowercase							=     model.priors[0]
							_lowercase							=     0
							_lowercase							=     top_prior.get_music_tokens_conds(lowercase__    ,			start=lowercase__    ,			end=start + top_prior.n_ctx)
							_lowercase							=     top_prior.get_metadata(labels[0].clone()    ,			lowercase__    ,			1_05_83_04    ,			0)

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

							_lowercase		,		_lowercase		,		_lowercase							=     top_prior.get_cond(lowercase__    ,			lowercase__)
							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 	_UpperCAmelCase							(		self	:						int)						->List[Any]:

							"""simple docstring"""



							_lowercase							=     False

							_lowercase							=     JukeboxModel.from_pretrained(self.model_id    ,			min_duration=0).eval()
							set_seed(0)
							_lowercase							=     torch.rand((1, 51_20, 1))
							_lowercase							=     list(self.prepare_inputs())

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

							_lowercase							=     torch.cat((zs[0], torch.zeros(1    ,			20_48 - zs[0].shape[-1]))    ,			dim=-1).long()
							_lowercase							=     [upper_a, model.vqvae.encode(lowercase__    ,			start_level=1    ,			bs_chunks=waveform.shape[0])[0], None]
							_lowercase							=     model._sample(
							    lowercase__    ,			lowercase__    ,			sample_levels=[1]    ,			save_results=lowercase__    ,			sample_length=40 * model.priors[1].raw_to_tokens)
							torch.testing.assert_allclose(zs[1][0][:40]    ,			torch.tensor(self.EXPECTED_PRIMED_1))

							_lowercase							=     torch.cat((zs[1], torch.zeros(1    ,			20_48 - zs[1].shape[-1]))    ,			dim=-1).long()
							_lowercase							=     [upper_a, upper_a, model.vqvae.encode(lowercase__    ,			start_level=0    ,			bs_chunks=waveform.shape[0])[0]]
							_lowercase							=     model._sample(
							    lowercase__    ,			lowercase__    ,			sample_levels=[2]    ,			save_results=lowercase__    ,			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 	_UpperCAmelCase							(		self	:						List[Any])						->List[Any]:

							"""simple docstring"""



							_lowercase							=     JukeboxModel.from_pretrained(self.model_id    ,			min_duration=0).eval()
							set_seed(0)
							_lowercase							=     torch.rand((1, 51_20, 1))
							with torch.no_grad():
								_lowercase							=     model.vqvae.encode(lowercase__    ,			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							=     model.vqvae.decode(lowercase__    ,			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 					__a   (		unittest.TestCase  ):
						__SCREAMING_SNAKE_CASE     :					str  =      (JukeboxModel,) if is_torch_available() else ()
						__SCREAMING_SNAKE_CASE     :					Optional[Any]  =      'openai/jukebox-5b-lyrics'
						__SCREAMING_SNAKE_CASE     :					Tuple  =      {
						    'artist': 'Zac Brown Band',
						    'genres': 'Country',
						    'lyrics': 'I met a traveller from an antique land,\n    Who said "Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ',
						}

						# fmt: off
						__SCREAMING_SNAKE_CASE     :					Tuple  =      [
						    1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
						    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
						    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
						    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
						    1_4_8_9, 1_4_8_9, 1_4_8_9, 1_4_8_9, 1_1_5_0, 1_8_5_3, 1_5_0_9, 1_1_5_0, 1_3_5_7, 1_5_0_9, 6, 1_2_7_2
						]
						__SCREAMING_SNAKE_CASE     :					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,
						    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3
						]

						__SCREAMING_SNAKE_CASE     :					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
						]
						__SCREAMING_SNAKE_CASE     :					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, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
						    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
						]

						__SCREAMING_SNAKE_CASE     :					Union[str, Any]  =      [
						    1_7_5_5, 1_0_6_1, 2_3_4, 1_7_5_5, 1_0_6_1, 1_7_5_5, 1_8_5, 2_9_0, 3_0_7, 3_0_7, 6_1_6, 6_1_6,
						    6_1_6, 6_1_6, 6_1_6, 6_1_6, 3_0_7, 2_9_0, 4_1_7, 1_7_5_5, 2_3_4, 1_7_5_5, 1_8_5, 2_9_0,
						    2_9_0, 2_9_0, 3_0_7, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 2_9_0, 2_3_4, 2_3_4, 1_7_5_5,
						    2_3_4, 2_3_4, 1_7_5_5, 2_3_4, 1_8_5, 1_8_5, 3_0_7, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 2_9_0,
						    1_7_5_5, 1_7_5_5, 1_7_5_5, 2_3_4, 2_3_4, 1_7_5_5, 1_5_7_2, 2_9_0, 3_0_7, 6_1_6, 3_4, 6_1_6
						]
						__SCREAMING_SNAKE_CASE     :					Union[str, 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, 1_8_5, 8_4_2, 5_9_1, 8_4_2,
						    1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 1_1_4, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 5_9_1,
						    8_9, 5_9_1, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 4_1_7, 1_3_7_2, 8_4_2, 1_3_7_2, 8_4_2, 3_4, 8_4_2,
						    1_8_5, 8_9, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 6_3_2
						]

						__SCREAMING_SNAKE_CASE     :					Tuple  =      [
						    1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
						    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
						    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
						    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
						    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3
						]
						__SCREAMING_SNAKE_CASE     :					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
						]

						__SCREAMING_SNAKE_CASE     :					List[Any]  =      [
						    1_1_2_5, 1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 1_1_2_5, 4_1_6,
						    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
						    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
						    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
						    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
						]
						__SCREAMING_SNAKE_CASE     :					Optional[Any]  =      [
						    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 	_UpperCAmelCase							(		self	:						Tuple    ,			lowercase__	:						Tuple)						->int:

							"""simple docstring"""



							_lowercase							=     JukeboxTokenizer.from_pretrained(lowercase__)
							_lowercase							=     tokenizer(**self.metas)["""input_ids"""]
							return tokens

						@slow
						def 	_UpperCAmelCase							(		self	:						int)						->List[Any]:

							"""simple docstring"""



							_lowercase							=     JukeboxModel.from_pretrained(self.model_id    ,			min_duration=0).eval()
							_lowercase							=     self.prepare_inputs(self.model_id)

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

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

							set_seed(0)
							_lowercase							=     model._sample(lowercase__    ,			lowercase__    ,			[2]    ,			sample_length=60 * model.priors[2].raw_to_tokens    ,			save_results=lowercase__)
							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 	_UpperCAmelCase							(		self	:						str)						->Union[str, Any]:

							"""simple docstring"""



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

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


						@slow
						def 	_UpperCAmelCase							(		self	:						Dict)						->Any:

							"""simple docstring"""



							_lowercase							=     """ArthurZ/jukebox_prior_0"""
							_lowercase							=     JukeboxPrior.from_pretrained(lowercase__    ,			min_duration=0).eval().half().to("""cuda""")

							_lowercase							=     self.prepare_inputs(lowercase__)[0].cuda()
							_lowercase							=     model.get_metadata(lowercase__    ,			0    ,			76_80    ,			0)
							set_seed(0)
							_lowercase							=     model.sample(1    ,			metadata=lowercase__    ,			sample_tokens=60)
							self.assertIn(outputs[0].cpu().tolist()    ,			[self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2])






"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import MobileNetVaImageProcessor
class SCREAMING_SNAKE_CASE       (      unittest.TestCase					):






	'''simple docstring'''







	def __init__(  self :Tuple       ,    __magic_name__ :str       ,    __magic_name__ :Optional[Any]=7       ,    __magic_name__ :int=3       ,    __magic_name__ :Union[str, Any]=18       ,    __magic_name__ :Union[str, Any]=30       ,    __magic_name__ :str=400       ,    __magic_name__ :str=True       ,    __magic_name__ :Any=None       ,    __magic_name__ :Tuple=True       ,    __magic_name__ :List[str]=None       ,    )			->   Optional[int]:

			'''simple docstring'''






			a__   		=     size if size is not None else {'''shortest_edge''': 20}
			a__   		=     crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
			a__   		=     parent
			a__   		=     batch_size
			a__   		=     num_channels
			a__   		=     image_size
			a__   		=     min_resolution
			a__   		=     max_resolution
			a__   		=     do_resize
			a__   		=     size
			a__   		=     do_center_crop
			a__   		=     crop_size






	def    _UpperCamelCase							(  self :Tuple			)			->   Any:

			'''simple docstring'''






			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			}

@require_torch
@require_vision
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					,   unittest.TestCase					):






	'''simple docstring'''







	snake_case__							:		str         =		MobileNetVaImageProcessor if is_vision_available() else None


	def    _UpperCamelCase							(  self :Union[str, Any]			)			->   str:

			'''simple docstring'''






			a__   		=     MobileNetVaImageProcessingTester(self			)


	@property
	def    _UpperCamelCase							(  self :List[str]			)			->   Optional[Any]:

			'''simple docstring'''






			return self.image_processor_tester.prepare_image_processor_dict()


	def    _UpperCamelCase							(  self :Tuple			)			->   Tuple:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			self.assertTrue(hasattr(__magic_name__       ,    '''do_resize'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''size'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''do_center_crop'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''center_crop'''			)			)


	def    _UpperCamelCase							(  self :List[Any]			)			->   int:

			'''simple docstring'''






			a__   		=     self.image_processing_class.from_dict(self.image_processor_dict			)
			self.assertEqual(image_processor.size       ,    {'''shortest_edge''': 20}			)
			self.assertEqual(image_processor.crop_size       ,    {'''height''': 18, '''width''': 18}			)

			a__   		=     self.image_processing_class.from_dict(self.image_processor_dict       ,    size=42       ,    crop_size=84			)
			self.assertEqual(image_processor.size       ,    {'''shortest_edge''': 42}			)
			self.assertEqual(image_processor.crop_size       ,    {'''height''': 84, '''width''': 84}			)


	def    _UpperCamelCase							(  self :Optional[int]			)			->   Any:

			'''simple docstring'''






			pass


	def    _UpperCamelCase							(  self :Dict			)			->   Tuple:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			# create random PIL images
			a__   		=     prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__magic_name__			)
			for image in image_inputs:
					self.assertIsInstance(__magic_name__       ,    Image.Image			)

			# Test not batched input
			a__   		=     image_processing(image_inputs[0]       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )

			# Test batched
			a__   		=     image_processing(__magic_name__       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )


	def    _UpperCamelCase							(  self :Optional[Any]			)			->   Union[str, Any]:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			# create random numpy tensors
			a__   		=     prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__magic_name__       ,    numpify=__magic_name__			)
			for image in image_inputs:
					self.assertIsInstance(__magic_name__       ,    np.ndarray			)

			# Test not batched input
			a__   		=     image_processing(image_inputs[0]       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )

			# Test batched
			a__   		=     image_processing(__magic_name__       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )






	def    _UpperCamelCase							(  self :List[Any]			)			->   str:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			# create random PyTorch tensors
			a__   		=     prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__magic_name__       ,    torchify=__magic_name__			)
			for image in image_inputs:
					self.assertIsInstance(__magic_name__       ,    torch.Tensor			)

			# Test not batched input
			a__   		=     image_processing(image_inputs[0]       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )

			# Test batched
			a__   		=     image_processing(__magic_name__       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )




