

from typing import Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather


@overload
def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						...


@overload
def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						...


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						snake_case_     =			aatype == rc.restype_order['''G''']
						snake_case_     =			rc.atom_order['''CA''']
						snake_case_     =			rc.atom_order['''CB''']
						snake_case_     =			torch.where(
						    is_gly[..., None].expand(*((-1,) * len(is_gly.shape ))						,	3 )						,	all_atom_positions[..., ca_idx, :]						,	all_atom_positions[..., cb_idx, :]						,	)

						if all_atom_masks is not None:
												snake_case_     =			torch.where(
												    SCREAMING_SNAKE_CASE__						,	all_atom_masks[..., ca_idx]						,	all_atom_masks[..., cb_idx]						,	)
												return pseudo_beta, pseudo_beta_mask
						else:
												return pseudo_beta


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						snake_case_     =			batched_gather(
						    SCREAMING_SNAKE_CASE__						,	batch['''residx_atom37_to_atom14''']						,	dim=-2						,	no_batch_dims=len(atomaa.shape[:-2] )						,	)

						snake_case_     =			atomaa_data * batch['''atom37_atom_exists'''][..., None]

						return atomaa_data


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						snake_case_     =			template_feats['''template_aatype''']
						snake_case_     =			template_feats['''template_torsion_angles_sin_cos''']
						snake_case_     =			template_feats['''template_alt_torsion_angles_sin_cos''']
						snake_case_     =			template_feats['''template_torsion_angles_mask''']
						snake_case_     =			torch.cat(
						    [
						        nn.functional.one_hot(SCREAMING_SNAKE_CASE__						,	22 ),
						        torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]						,	14 ),
						        alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]						,	14 ),
						        torsion_angles_mask,
						    ]						,	dim=-1						,	)

						return template_angle_feat


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = 1E-20						,	SCREAMING_SNAKE_CASE__ = 1E8						,	):
						snake_case_     =			batch['''template_pseudo_beta_mask''']
						snake_case_     =			template_mask[..., None] * template_mask[..., None, :]

						# Compute distogram (this seems to differ slightly from Alg. 5)
						snake_case_     =			batch['''template_pseudo_beta''']
						snake_case_     =			torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2						,	dim=-1						,	keepdim=SCREAMING_SNAKE_CASE__ )
						snake_case_     =			torch.linspace(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	device=tpb.device ) ** 2
						snake_case_     =			torch.cat([lower[1:], lower.new_tensor([inf] )]						,	dim=-1 )
						snake_case_     =			((dgram > lower) * (dgram < upper)).type(dgram.dtype )

						snake_case_     =			[dgram, template_mask_ad[..., None]]

						snake_case_     =			nn.functional.one_hot(
						    batch['''template_aatype''']						,	rc.restype_num + 2						,	)

						snake_case_     =			batch['''template_aatype'''].shape[-1]
						to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]						,	SCREAMING_SNAKE_CASE__						,	-1						,	-1 ) )
						to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]						,	-1						,	SCREAMING_SNAKE_CASE__						,	-1 ) )

						snake_case_,					snake_case_,					snake_case_     =			[rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
						snake_case_     =			Rigid.make_transform_from_reference(
						    n_xyz=batch['''template_all_atom_positions'''][..., n, :]						,	ca_xyz=batch['''template_all_atom_positions'''][..., ca, :]						,	c_xyz=batch['''template_all_atom_positions'''][..., c, :]						,	eps=SCREAMING_SNAKE_CASE__						,	)
						snake_case_     =			rigids.get_trans()[..., None, :, :]
						snake_case_     =			rigids[..., None].invert_apply(SCREAMING_SNAKE_CASE__ )

						snake_case_     =			torch.rsqrt(eps + torch.sum(rigid_vec**2						,	dim=-1 ) )

						snake_case_     =			batch['''template_all_atom_mask''']
						snake_case_     =			t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
						snake_case_     =			template_mask[..., None] * template_mask[..., None, :]

						snake_case_     =			inv_distance_scalar * template_mask_ad
						snake_case_     =			rigid_vec * inv_distance_scalar[..., None]

						if not use_unit_vector:
												snake_case_     =			unit_vector * 0.0

						to_concat.extend(torch.unbind(unit_vector[..., None, :]						,	dim=-1 ) )
						to_concat.append(template_mask_ad[..., None] )

						snake_case_     =			torch.cat(SCREAMING_SNAKE_CASE__						,	dim=-1 )
						snake_case_     =			act * template_mask_ad[..., None]

						return act


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__ ):
						snake_case_     =			nn.functional.one_hot(batch['''extra_msa''']						,	23 )
						snake_case_     =			[
						    msa_ahot,
						    batch['''extra_has_deletion'''].unsqueeze(-1 ),
						    batch['''extra_deletion_value'''].unsqueeze(-1 ),
						]
						return torch.cat(SCREAMING_SNAKE_CASE__						,	dim=-1 )


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	):
						snake_case_     =			rrgdf[aatype, ...]

						# [*, N, 8] transformations, i.e.
						#   One [*, N, 8, 3, 3] rotation matrix and
						#   One [*, N, 8, 3]    translation matrix
						snake_case_     =			r.from_tensor_axa(SCREAMING_SNAKE_CASE__ )

						snake_case_     =			alpha.new_zeros((*((1,) * len(alpha.shape[:-1] )), 2) )
						snake_case_     =			1

						# [*, N, 8, 2]
						snake_case_     =			torch.cat([bb_rot.expand(*alpha.shape[:-2]						,	-1						,	-1 ), alpha]						,	dim=-2 )

						# [*, N, 8, 3, 3]
						# Produces rotation matrices of the form:
						# [
						#   [1, 0  , 0  ],
						#   [0, a_2,-a_1],
						#   [0, a_1, a_2]
						# ]
						# This follows the original code rather than the supplement, which uses
						# different indices.

						snake_case_     =			alpha.new_zeros(default_r.get_rots().get_rot_mats().shape )
						snake_case_     =			1
						snake_case_     =			alpha[..., 1]
						snake_case_     =			-alpha[..., 0]
						snake_case_     =			alpha

						snake_case_     =			default_r.compose(Rigid(Rotation(rot_mats=SCREAMING_SNAKE_CASE__ )						,	SCREAMING_SNAKE_CASE__ ) )

						snake_case_     =			all_frames[..., 5]
						snake_case_     =			all_frames[..., 6]
						snake_case_     =			all_frames[..., 7]

						snake_case_     =			all_frames[..., 4]
						snake_case_     =			chia_frame_to_bb.compose(SCREAMING_SNAKE_CASE__ )
						snake_case_     =			chia_frame_to_bb.compose(SCREAMING_SNAKE_CASE__ )
						snake_case_     =			chia_frame_to_bb.compose(SCREAMING_SNAKE_CASE__ )

						snake_case_     =			Rigid.cat(
						    [
						        all_frames[..., :5],
						        chia_frame_to_bb.unsqueeze(-1 ),
						        chia_frame_to_bb.unsqueeze(-1 ),
						        chia_frame_to_bb.unsqueeze(-1 ),
						    ]						,	dim=-1						,	)

						snake_case_     =			r[..., None].compose(SCREAMING_SNAKE_CASE__ )

						return all_frames_to_global


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	):
						snake_case_     =			group_idx[aatype, ...]

						# [*, N, 14, 8]
						snake_case_     =			nn.functional.one_hot(
						    SCREAMING_SNAKE_CASE__						,	num_classes=default_frames.shape[-3]						,	)

						# [*, N, 14, 8]
						snake_case_     =			r[..., None, :] * group_mask_one_hot

						# [*, N, 14]
						snake_case_     =			t_atoms_to_global.map_tensor_fn(lambda SCREAMING_SNAKE_CASE__ : torch.sum(SCREAMING_SNAKE_CASE__						,	dim=-1 ) )

						# [*, N, 14, 1]
						snake_case_     =			atom_mask[aatype, ...].unsqueeze(-1 )

						# [*, N, 14, 3]
						snake_case_     =			lit_positions[aatype, ...]
						snake_case_     =			t_atoms_to_global.apply(SCREAMING_SNAKE_CASE__ )
						snake_case_     =			pred_positions * atom_mask

						return pred_positions


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 			snake_case_     (	unittest.TestCase      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       (JukeboxModel,) if is_torch_available() else ()
			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       "openai/jukebox-1b-lyrics"
			SCREAMING_SNAKE_CASE  :     Union[str, 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  :     int		    =       [
			    1864, 1536, 1213, 1870, 1357, 1536, 519, 880, 1323, 789, 1082, 534,
			    1000, 1445, 1105, 1130, 967, 515, 1434, 1620, 534, 1495, 283, 1445,
			    333, 1307, 539, 1631, 1528, 375, 1434, 673, 627, 710, 778, 1883,
			    1405, 1276, 1455, 1228
			]

			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       [
			    1489, 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
			]

			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       [
			    1125, 1751, 697, 1776, 1141, 1476, 391, 697, 1125, 684, 867, 416,
			    844, 1372, 1274, 717, 1274, 844, 1299, 1419, 697, 1370, 317, 1125,
			    191, 1440, 1370, 1440, 1370, 282, 1621, 1370, 368, 349, 867, 1872,
			    1262, 869, 1728, 747
			]
			SCREAMING_SNAKE_CASE  :     List[Any]		    =       [
			    416, 416, 1125, 1125, 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
			]

			SCREAMING_SNAKE_CASE  :     Tuple		    =       [
			    1755, 842, 307, 1843, 1022, 1395, 234, 1554, 806, 739, 1022, 442,
			    616, 556, 268, 1499, 933, 457, 1440, 1837, 755, 985, 308, 902,
			    293, 1443, 1671, 1141, 1533, 555, 1562, 1061, 287, 417, 1022, 2008,
			    1186, 1015, 1777, 268
			]
			SCREAMING_SNAKE_CASE  :     Union[str, Any]		    =       [
			    854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842,
			    185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842,
			    591, 842, 1353, 842, 185, 842, 591, 842, 591, 114, 591, 842,
			    185, 842, 591, 89
			]

			SCREAMING_SNAKE_CASE  :     Dict		    =       [1058304, 0, 786432, 7169, 507, 76, 27, 40, 30, 76]

			SCREAMING_SNAKE_CASE  :     str		    =       [
			    390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002,
			    1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002,
			    1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907,
			    1907, 1788, 596, 1626
			]
			SCREAMING_SNAKE_CASE  :     List[Any]		    =       [
			    1236, 1668, 1484, 1920, 1848, 1409, 139, 864, 1828, 1272, 1599, 824,
			    1672, 139, 555, 1484, 824, 1920, 555, 596, 1579, 1599, 1231, 1599,
			    1637, 1407, 212, 824, 1599, 116, 1433, 824, 258, 1599, 1433, 1895,
			    1063, 1433, 1433, 1599
			]
			SCREAMING_SNAKE_CASE  :     Tuple		    =       [
			    1684, 1873, 1119, 1189, 395, 611, 1901, 972, 890, 1337, 1392, 1927,
			    96, 972, 672, 780, 1119, 890, 158, 771, 1073, 1927, 353, 1331,
			    1269, 1459, 1333, 1645, 812, 1577, 1337, 606, 353, 981, 1466, 619,
			    197, 391, 302, 1930
			]
			SCREAMING_SNAKE_CASE  :     List[str]		    =       [
			    390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002,
			    1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002,
			    1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907,
			    1907, 1788, 596, 1626
			]
			SCREAMING_SNAKE_CASE  :     int		    =       [
			    -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  :     Dict		    =       [
			    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  :     List[str]		    =       [
			    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		    =       [
			    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        snake_case__(  self				:       Union[str, Any]							)   ->str:
									snake_case_     =			JukeboxTokenizer.from_pretrained(self.model_id							)
									snake_case_     =			tokenizer(**self.metas							)['''input_ids''']
									return tokens







			@slow
			def        snake_case__(  self				:       int							)   ->str:
									snake_case_     =			JukeboxModel.from_pretrained(self.model_id							,     min_duration=0							).eval()
									snake_case_     =			self.prepare_inputs()

									set_seed(0							)
									snake_case_     =			[torch.zeros(1							,     0							,     dtype=torch.long							).cpu() for _ in range(3							)]
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [0]							,     sample_length=4_0 * 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							)
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [1]							,     sample_length=4_0 * 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							)
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [2]							,     sample_length=4_0 * 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        snake_case__(  self				:       Union[str, Any]							)   ->List[str]:
									snake_case_     =			False
									snake_case_     =			JukeboxModel.from_pretrained(self.model_id							,     min_duration=0							).eval()

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

									snake_case_     =			model.priors[0]
									snake_case_     =			0
									snake_case_     =			top_prior.get_music_tokens_conds(_UpperCamelCase							,     start=_UpperCamelCase							,     end=start + top_prior.n_ctx							)
									snake_case_     =			top_prior.get_metadata(labels[0].clone()							,     _UpperCamelCase							,     1_0_5_8_3_0_4							,     0							)

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

									snake_case_,					snake_case_,					snake_case_     =			top_prior.get_cond(_UpperCamelCase							,     _UpperCamelCase							)
									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        snake_case__(  self				:       Union[str, Any]							)   ->List[Any]:
									snake_case_     =			False

									snake_case_     =			JukeboxModel.from_pretrained(self.model_id							,     min_duration=0							).eval()
									set_seed(0							)
									snake_case_     =			torch.rand((1, 5_1_2_0, 1)							)
									snake_case_     =			list(self.prepare_inputs()							)

									snake_case_     =			[model.vqvae.encode(_UpperCamelCase							,     start_level=2							,     bs_chunks=waveform.shape[0]							)[0], None, None]
									snake_case_     =			model._sample(
									    _UpperCamelCase							,     _UpperCamelCase							,     sample_levels=[0]							,     save_results=_UpperCamelCase							,     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							)							)

									snake_case_     =			torch.cat((zs[0], torch.zeros(1							,     2_0_4_8 - zs[0].shape[-1]							))							,     dim=-1							).long()
									snake_case_     =			[upper_a, model.vqvae.encode(_UpperCamelCase							,     start_level=1							,     bs_chunks=waveform.shape[0]							)[0], None]
									snake_case_     =			model._sample(
									    _UpperCamelCase							,     _UpperCamelCase							,     sample_levels=[1]							,     save_results=_UpperCamelCase							,     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							)							)

									snake_case_     =			torch.cat((zs[1], torch.zeros(1							,     2_0_4_8 - zs[1].shape[-1]							))							,     dim=-1							).long()
									snake_case_     =			[upper_a, upper_a, model.vqvae.encode(_UpperCamelCase							,     start_level=0							,     bs_chunks=waveform.shape[0]							)[0]]
									snake_case_     =			model._sample(
									    _UpperCamelCase							,     _UpperCamelCase							,     sample_levels=[2]							,     save_results=_UpperCamelCase							,     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        snake_case__(  self				:       Union[str, Any]							)   ->Optional[Any]:
									snake_case_     =			JukeboxModel.from_pretrained(self.model_id							,     min_duration=0							).eval()
									set_seed(0							)
									snake_case_     =			torch.rand((1, 5_1_2_0, 1)							)
									with torch.no_grad():
															snake_case_     =			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():
															snake_case_     =			model.vqvae.decode(_UpperCamelCase							,     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 			snake_case_     (	unittest.TestCase      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     Any		    =       (JukeboxModel,) if is_torch_available() else ()
			SCREAMING_SNAKE_CASE  :     Optional[Any]		    =       "openai/jukebox-5b-lyrics"
			SCREAMING_SNAKE_CASE  :     int		    =       {
			    "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		    =       [
			    1489, 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,
			    1489, 1489, 1489, 1489, 1150, 1853, 1509, 1150, 1357, 1509, 6, 1272
			]
			SCREAMING_SNAKE_CASE  :     List[str]		    =       [
			    1489, 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
			]

			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       [
			    1125, 416, 1125, 1125, 1125, 1125, 1125, 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
			]
			SCREAMING_SNAKE_CASE  :     Dict		    =       [
			    416, 416, 1125, 1125, 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
			]

			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       [
			    1755, 1061, 234, 1755, 1061, 1755, 185, 290, 307, 307, 616, 616,
			    616, 616, 616, 616, 307, 290, 417, 1755, 234, 1755, 185, 290,
			    290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1755,
			    234, 234, 1755, 234, 185, 185, 307, 616, 616, 616, 616, 290,
			    1755, 1755, 1755, 234, 234, 1755, 1572, 290, 307, 616, 34, 616
			]
			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       [
			    854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842, 185,
			    417, 185, 842, 307, 842, 591, 842, 185, 842, 185, 842, 591, 842,
			    1353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591,
			    89, 591, 842, 591, 842, 591, 417, 1372, 842, 1372, 842, 34, 842,
			    185, 89, 591, 842, 185, 842, 591, 632
			]

			SCREAMING_SNAKE_CASE  :     Any		    =       [
			    1489, 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
			]
			SCREAMING_SNAKE_CASE  :     Optional[Any]		    =       [
			    1489, 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, 1853, 1177, 1536, 1228,
			    710, 475, 1489, 1229, 1224, 231, 1224, 252, 1434, 653, 475,
			    1106, 1877, 1599, 1228, 1600, 1683, 1182, 1853, 475, 1864,
			    252, 1229, 1434, 2001
			]

			SCREAMING_SNAKE_CASE  :     List[str]		    =       [
			    1125, 1125, 416, 1125, 1125, 416, 1125, 1125, 416, 416, 1125, 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
			]
			SCREAMING_SNAKE_CASE  :     Optional[Any]		    =       [
			    491, 1755, 34, 1613, 1755, 417, 992, 1613, 222, 842, 1353, 1613,
			    844, 632, 185, 1613, 844, 632, 185, 1613, 185, 842, 677, 1613,
			    185, 114, 1353, 1613, 307, 89, 844, 1613, 307, 1332, 234, 1979,
			    307, 89, 1353, 616, 34, 842, 185, 842, 34, 842, 185, 842,
			    307, 114, 185, 89, 34, 1268, 185, 89, 34, 842, 185, 89
			]







			def        snake_case__(  self				:       List[Any]							,     _UpperCamelCase				:       List[str]							)   ->List[Any]:
									snake_case_     =			JukeboxTokenizer.from_pretrained(_UpperCamelCase							)
									snake_case_     =			tokenizer(**self.metas							)['''input_ids''']
									return tokens







			@slow
			def        snake_case__(  self				:       List[Any]							)   ->Any:
									snake_case_     =			JukeboxModel.from_pretrained(self.model_id							,     min_duration=0							).eval()
									snake_case_     =			self.prepare_inputs(self.model_id							)

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

									set_seed(0							)
									model.priors[0].cuda()
									snake_case_     =			[torch.zeros(1							,     0							,     dtype=torch.long							).cuda() for _ in range(3							)]
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [0]							,     sample_length=6_0 * 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()
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [1]							,     sample_length=6_0 * 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()
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [2]							,     sample_length=6_0 * 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        snake_case__(  self				:       List[Any]							)   ->List[Any]:
									snake_case_     =			'''ArthurZ/jukebox_prior_0'''
									snake_case_     =			JukeboxPrior.from_pretrained(_UpperCamelCase							,     min_duration=0							).eval().half().to('''cuda'''							)

									snake_case_     =			self.prepare_inputs(_UpperCamelCase							)[0].cuda()
									snake_case_     =			model.get_metadata(_UpperCamelCase							,     0							,     7_6_8_0							,     0							)
									set_seed(0							)
									snake_case_     =			model.sample(1							,     metadata=_UpperCamelCase							,     sample_tokens=6_0							)
									self.assertIn(outputs[0].cpu().tolist()							,     [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]							)