





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

					UpperCamelCase								=							[
					    1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
					    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
					    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
					    6_5_3, 6_5_3, 6_5_3, 6_5_3
					]

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

					UpperCamelCase								=							[
					    1_7_5_5, 8_4_2, 3_0_7, 1_8_4_3, 1_0_2_2, 1_3_9_5, 2_3_4, 1_5_5_4, 8_0_6, 7_3_9, 1_0_2_2, 4_4_2,
					    6_1_6, 5_5_6, 2_6_8, 1_4_9_9, 9_3_3, 4_5_7, 1_4_4_0, 1_8_3_7, 7_5_5, 9_8_5, 3_0_8, 9_0_2,
					    2_9_3, 1_4_4_3, 1_6_7_1, 1_1_4_1, 1_5_3_3, 5_5_5, 1_5_6_2, 1_0_6_1, 2_8_7, 4_1_7, 1_0_2_2, 2_0_0_8,
					    1_1_8_6, 1_0_1_5, 1_7_7_7, 2_6_8
					]
					UpperCamelCase								=							[
					    8_5_4, 8_4_2, 1_3_5_3, 1_1_4, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 1_1_4, 5_9_1, 8_4_2,
					    1_8_5, 4_1_7, 1_8_5, 8_4_2, 3_0_7, 8_4_2, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 3_0_7, 8_4_2,
					    5_9_1, 8_4_2, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 1_1_4, 5_9_1, 8_4_2,
					    1_8_5, 8_4_2, 5_9_1, 8_9
					]

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

					UpperCamelCase								=							[
					    3_9_0, 1_1_6_0, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_9_0_7, 1_0_0_2, 1_0_0_2, 1_8_5_4, 1_0_0_2,
					    1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_1_6_0, 1_1_6_0, 1_6_0_6, 5_9_6, 5_9_6, 1_1_6_0, 1_0_0_2,
					    1_5_1_6, 5_9_6, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_8_5_4, 1_7_8_8, 1_9_0_7,
					    1_9_0_7, 1_7_8_8, 5_9_6, 1_6_2_6
					]
					UpperCamelCase								=							[
					    1_2_3_6, 1_6_6_8, 1_4_8_4, 1_9_2_0, 1_8_4_8, 1_4_0_9, 1_3_9, 8_6_4, 1_8_2_8, 1_2_7_2, 1_5_9_9, 8_2_4,
					    1_6_7_2, 1_3_9, 5_5_5, 1_4_8_4, 8_2_4, 1_9_2_0, 5_5_5, 5_9_6, 1_5_7_9, 1_5_9_9, 1_2_3_1, 1_5_9_9,
					    1_6_3_7, 1_4_0_7, 2_1_2, 8_2_4, 1_5_9_9, 1_1_6, 1_4_3_3, 8_2_4, 2_5_8, 1_5_9_9, 1_4_3_3, 1_8_9_5,
					    1_0_6_3, 1_4_3_3, 1_4_3_3, 1_5_9_9
					]
					UpperCamelCase								=							[
					    1_6_8_4, 1_8_7_3, 1_1_1_9, 1_1_8_9, 3_9_5, 6_1_1, 1_9_0_1, 9_7_2, 8_9_0, 1_3_3_7, 1_3_9_2, 1_9_2_7,
					    9_6, 9_7_2, 6_7_2, 7_8_0, 1_1_1_9, 8_9_0, 1_5_8, 7_7_1, 1_0_7_3, 1_9_2_7, 3_5_3, 1_3_3_1,
					    1_2_6_9, 1_4_5_9, 1_3_3_3, 1_6_4_5, 8_1_2, 1_5_7_7, 1_3_3_7, 6_0_6, 3_5_3, 9_8_1, 1_4_6_6, 6_1_9,
					    1_9_7, 3_9_1, 3_0_2, 1_9_3_0
					]
					UpperCamelCase								=							[
					    3_9_0, 1_1_6_0, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_9_0_7, 1_0_0_2, 1_0_0_2, 1_8_5_4, 1_0_0_2,
					    1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_1_6_0, 1_1_6_0, 1_6_0_6, 5_9_6, 5_9_6, 1_1_6_0, 1_0_0_2,
					    1_5_1_6, 5_9_6, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_8_5_4, 1_7_8_8, 1_9_0_7,
					    1_9_0_7, 1_7_8_8, 5_9_6, 1_6_2_6
					]
					UpperCamelCase								=							[
					    -0.0492, -0.0524, -0.0565, -0.0640, -0.0686, -0.0684, -0.0677, -0.0664,
					    -0.0605, -0.0490, -0.0330, -0.0168, -0.0083, -0.0075, -0.0051, 0.0025,
					    0.0136, 0.0261, 0.0386, 0.0497, 0.0580, 0.0599, 0.0583, 0.0614,
					    0.0740, 0.0889, 0.1023, 0.1162, 0.1211, 0.1212, 0.1251, 0.1336,
					    0.1502, 0.1686, 0.1883, 0.2148, 0.2363, 0.2458, 0.2507, 0.2531
					]
					UpperCamelCase								=							[
					    0.0256, -0.0544, 0.1600, -0.0032, 0.1066, 0.0825, -0.0013, 0.3440,
					    0.0210, 0.0412, -0.1777, -0.0892, -0.0164, 0.0285, -0.0613, -0.0617,
					    -0.0137, -0.0201, -0.0175, 0.0215, -0.0627, 0.0520, -0.0730, 0.0970,
					    -0.0100, 0.0442, -0.0586, 0.0207, -0.0015, -0.0082
					]
					UpperCamelCase								=							[
					    0.0415, 0.0877, 0.0022, -0.0055, 0.0751, 0.0334, 0.0324, -0.0068,
					    0.0011, 0.0017, -0.0676, 0.0655, -0.0143, 0.0399, 0.0303, 0.0743,
					    -0.0168, -0.0394, -0.1113, 0.0124, 0.0442, 0.0267, -0.0003, -0.1536,
					    -0.0116, -0.1837, -0.0180, -0.1026, -0.0777, -0.0456
					]
					UpperCamelCase								=							[
					    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      lowerCamelCase	( self      :int		):
												A		=  JukeboxTokenizer.from_pretrained(self.model_id		)
												A		=  tokenizer(**self.metas		)["input_ids"]
												return tokens


					@slow
					def      lowerCamelCase	( self      :Union[str, Any]		):
												A		=  JukeboxModel.from_pretrained(self.model_id					,	min_duration=0		).eval()
												A		=  self.prepare_inputs()

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

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

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


					@slow
					def      lowerCamelCase	( self      :str		):
												A		=  False
												A		=  JukeboxModel.from_pretrained(self.model_id					,	min_duration=0		).eval()

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

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

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

												A,     A,     A		=  top_prior.get_cond(__UpperCamelCase					,	__UpperCamelCase		)
												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      lowerCamelCase	( self      :List[str]		):
												A		=  False

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

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

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

												A		=  torch.cat((zs[1], torch.zeros(1					,	20_48 - zs[1].shape[-1]		))					,	dim=-1		).long()
												A		=  [upper_a, upper_a, model.vqvae.encode(__UpperCamelCase					,	start_level=0					,	bs_chunks=waveform.shape[0]		)[0]]
												A		=  model._sample(
												    __UpperCamelCase					,	__UpperCamelCase					,	sample_levels=[2]					,	save_results=__UpperCamelCase					,	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      lowerCamelCase	( self      :Dict		):
												A		=  JukeboxModel.from_pretrained(self.model_id					,	min_duration=0		).eval()
												set_seed(0		)
												A		=  torch.rand((1, 51_20, 1)		)
												with torch.no_grad():
																			A		=  model.vqvae.encode(__UpperCamelCase					,	start_level=2					,	bs_chunks=x.shape[0]		)
												torch.testing.assert_allclose(zs[0][0]					,	torch.tensor(self.EXPECTED_VQVAE_ENCODE		)		)

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

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

					UpperCamelCase								=							[
					    1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 1_1_2_5, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
					    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
					    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
					    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
					    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
					]
					UpperCamelCase								=							[
					    4_1_6, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
					    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
					    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
					    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
					    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
					]

					UpperCamelCase								=							[
					    1_7_5_5, 1_0_6_1, 2_3_4, 1_7_5_5, 1_0_6_1, 1_7_5_5, 1_8_5, 2_9_0, 3_0_7, 3_0_7, 6_1_6, 6_1_6,
					    6_1_6, 6_1_6, 6_1_6, 6_1_6, 3_0_7, 2_9_0, 4_1_7, 1_7_5_5, 2_3_4, 1_7_5_5, 1_8_5, 2_9_0,
					    2_9_0, 2_9_0, 3_0_7, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 2_9_0, 2_3_4, 2_3_4, 1_7_5_5,
					    2_3_4, 2_3_4, 1_7_5_5, 2_3_4, 1_8_5, 1_8_5, 3_0_7, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 2_9_0,
					    1_7_5_5, 1_7_5_5, 1_7_5_5, 2_3_4, 2_3_4, 1_7_5_5, 1_5_7_2, 2_9_0, 3_0_7, 6_1_6, 3_4, 6_1_6
					]
					UpperCamelCase								=							[
					    8_5_4, 8_4_2, 1_3_5_3, 1_1_4, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 1_1_4, 5_9_1, 8_4_2, 1_8_5,
					    4_1_7, 1_8_5, 8_4_2, 3_0_7, 8_4_2, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2,
					    1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 1_1_4, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 5_9_1,
					    8_9, 5_9_1, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 4_1_7, 1_3_7_2, 8_4_2, 1_3_7_2, 8_4_2, 3_4, 8_4_2,
					    1_8_5, 8_9, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 6_3_2
					]

					UpperCamelCase								=							[
					    1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
					    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
					    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
					    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
					    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3
					]
					UpperCamelCase								=							[
					    1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
					    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
					    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 1_8_5_3, 1_1_7_7, 1_5_3_6, 1_2_2_8,
					    7_1_0, 4_7_5, 1_4_8_9, 1_2_2_9, 1_2_2_4, 2_3_1, 1_2_2_4, 2_5_2, 1_4_3_4, 6_5_3, 4_7_5,
					    1_1_0_6, 1_8_7_7, 1_5_9_9, 1_2_2_8, 1_6_0_0, 1_6_8_3, 1_1_8_2, 1_8_5_3, 4_7_5, 1_8_6_4,
					    2_5_2, 1_2_2_9, 1_4_3_4, 2_0_0_1
					]

					UpperCamelCase								=							[
					    1_1_2_5, 1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 1_1_2_5, 4_1_6,
					    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
					    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
					    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
					    4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
					]
					UpperCamelCase								=							[
					    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      lowerCamelCase	( self      :Optional[Any]					,	__UpperCamelCase      :Union[str, Any]		):
												A		=  JukeboxTokenizer.from_pretrained(__UpperCamelCase		)
												A		=  tokenizer(**self.metas		)["input_ids"]
												return tokens


					@slow
					def      lowerCamelCase	( self      :List[str]		):
												A		=  JukeboxModel.from_pretrained(self.model_id					,	min_duration=0		).eval()
												A		=  self.prepare_inputs(self.model_id		)

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

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

												set_seed(0		)
												A		=  model._sample(__UpperCamelCase					,	__UpperCamelCase					,	[2]					,	sample_length=60 * model.priors[2].raw_to_tokens					,	save_results=__UpperCamelCase		)
												self.assertIn(zs[2][0].detach().cpu().tolist()					,	[self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]		)


					@slow
					@skip("Not enough GPU memory on CI runners"		)
					def      lowerCamelCase	( self      :int		):
												A		=  JukeboxModel.from_pretrained(self.model_id					,	min_duration=0		).eval()
												A		=  [i.cuda() for i in self.prepare_inputs(self.model_id		)]

												set_seed(0		)
												model.priors[0].cuda()
												A		=  [torch.zeros(1					,	0					,	dtype=torch.long		).cuda() for _ in range(3		)]
												A		=  model._sample(__UpperCamelCase					,	__UpperCamelCase					,	[0]					,	sample_length=60 * model.priors[0].raw_to_tokens					,	save_results=__UpperCamelCase		)
												torch.testing.assert_allclose(zs[0][0].cpu()					,	torch.tensor(self.EXPECTED_GPU_OUTPUTS_2		)		)
												model.priors[0].cpu()

												set_seed(0		)
												model.priors[1].cuda()
												A		=  model._sample(__UpperCamelCase					,	__UpperCamelCase					,	[1]					,	sample_length=60 * model.priors[1].raw_to_tokens					,	save_results=__UpperCamelCase		)
												torch.testing.assert_allclose(zs[1][0].cpu()					,	torch.tensor(self.EXPECTED_GPU_OUTPUTS_1		)		)
												model.priors[1].cpu()

												set_seed(0		)
												model.priors[2].cuda()
												A		=  model._sample(__UpperCamelCase					,	__UpperCamelCase					,	[2]					,	sample_length=60 * model.priors[2].raw_to_tokens					,	save_results=__UpperCamelCase		)
												torch.testing.assert_allclose(zs[2][0].cpu()					,	torch.tensor(self.EXPECTED_GPU_OUTPUTS_0		)		)


					@slow
					def      lowerCamelCase	( self      :Union[str, Any]		):
												A		=  "ArthurZ/jukebox_prior_0"
												A		=  JukeboxPrior.from_pretrained(__UpperCamelCase					,	min_duration=0		).eval().half().to("cuda"		)

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









"""simple docstring"""
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							['''image_processor''', '''tokenizer''']
					UpperCamelCase								=							'''LayoutLMv2ImageProcessor'''
					UpperCamelCase								=							('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')


					def __init__( self      :List[Any]					,	__UpperCamelCase      :List[Any]=None					,	__UpperCamelCase      :Tuple=None					,	**__UpperCamelCase      :int		):
												A		=  None
												if "feature_extractor" in kwargs:
																			warnings.warn(
																			    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																			    " instead."					,	__UpperCamelCase					,	)
																			A		=  kwargs.pop("feature_extractor"		)

												A		=  image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																			raise ValueError("You need to specify an `image_processor`."		)
												if tokenizer is None:
																			raise ValueError("You need to specify a `tokenizer`."		)

												super().__init__(__UpperCamelCase					,	__UpperCamelCase		)


					def __call__( self      :Optional[Any]					,	__UpperCamelCase      :int					,	__UpperCamelCase      :Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,	__UpperCamelCase      :Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None					,	__UpperCamelCase      :Union[List[List[int]], List[List[List[int]]]] = None					,	__UpperCamelCase      :Optional[Union[List[int], List[List[int]]]] = None					,	__UpperCamelCase      :bool = True					,	__UpperCamelCase      :Union[bool, str, PaddingStrategy] = False					,	__UpperCamelCase      :Union[bool, str, TruncationStrategy] = False					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :int = 0					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :Optional[bool] = None					,	__UpperCamelCase      :Optional[bool] = None					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = True					,	__UpperCamelCase      :Optional[Union[str, TensorType]] = None					,	**__UpperCamelCase      :int					,	):
												# verify input
												if self.image_processor.apply_ocr and (boxes is not None):
																			raise ValueError(
																			    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."		)

												if self.image_processor.apply_ocr and (word_labels is not None):
																			raise ValueError(
																			    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."		)

												if return_overflowing_tokens is True and return_offsets_mapping is False:
																			raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."		)

												# first, apply the image processor
												A		=  self.image_processor(images=__UpperCamelCase					,	return_tensors=__UpperCamelCase		)

												# second, apply the tokenizer
												if text is not None and self.image_processor.apply_ocr and text_pair is None:
																			if isinstance(__UpperCamelCase					,	__UpperCamelCase		):
																										A		=  [text]  # add batch dimension (as the image processor always adds a batch dimension)
																			A		=  features["words"]

												A		=  self.tokenizer(
												    text=text if text is not None else features["words"]					,	text_pair=text_pair if text_pair is not None else None					,	boxes=boxes if boxes is not None else features["boxes"]					,	word_labels=__UpperCamelCase					,	add_special_tokens=__UpperCamelCase					,	padding=__UpperCamelCase					,	truncation=__UpperCamelCase					,	max_length=__UpperCamelCase					,	stride=__UpperCamelCase					,	pad_to_multiple_of=__UpperCamelCase					,	return_token_type_ids=__UpperCamelCase					,	return_attention_mask=__UpperCamelCase					,	return_overflowing_tokens=__UpperCamelCase					,	return_special_tokens_mask=__UpperCamelCase					,	return_offsets_mapping=__UpperCamelCase					,	return_length=__UpperCamelCase					,	verbose=__UpperCamelCase					,	return_tensors=__UpperCamelCase					,	**__UpperCamelCase					,	)

												# add pixel values
												A		=  features.pop("pixel_values"		)
												if return_overflowing_tokens is True:
																			A		=  self.get_overflowing_images(__UpperCamelCase					,	encoded_inputs["overflow_to_sample_mapping"]		)
												A		=  images

												return encoded_inputs


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :List[str]		):
												# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
												A		=  []
												for sample_idx in overflow_to_sample_mapping:
																			images_with_overflow.append(images[sample_idx]		)

												if len(__UpperCamelCase		) != len(__UpperCamelCase		):
																			raise ValueError(
																			    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
																			    f" {len(__UpperCamelCase		)} and {len(__UpperCamelCase		)}"		)

												return images_with_overflow


					def      lowerCamelCase	( self      :Tuple					,	*__UpperCamelCase      :Union[str, Any]					,	**__UpperCamelCase      :Tuple		):
												return self.tokenizer.batch_decode(*__UpperCamelCase					,	**__UpperCamelCase		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	*__UpperCamelCase      :str					,	**__UpperCamelCase      :List[str]		):
												return self.tokenizer.decode(*__UpperCamelCase					,	**__UpperCamelCase		)


					@property
					def      lowerCamelCase	( self      :str		):
												return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]


					@property
					def      lowerCamelCase	( self      :str		):
												warnings.warn(
												    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,	__UpperCamelCase					,	)
												return self.image_processor_class


					@property
					def      lowerCamelCase	( self      :int		):
												warnings.warn(
												    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,	__UpperCamelCase					,	)
												return self.image_processor


