



'''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 			__magic_name__		(					unittest.TestCase     ):
							lowerCAmelCase :					str            =      (JukeboxModel,) if is_torch_available() else ()
							lowerCAmelCase :					Union[str, Any]            =      'openai/jukebox-1b-lyrics'
							lowerCAmelCase :					List[str]            =      {
							    '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 :					List[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
							]

							lowerCAmelCase :					List[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, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
							    6_5_3, 6_5_3, 6_5_3, 6_5_3
							]

							lowerCAmelCase :					Optional[Any]            =      [
							    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
							]
							lowerCAmelCase :					Optional[int]            =      [
							    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
							]

							lowerCAmelCase :					int            =      [
							    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
							]
							lowerCAmelCase :					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
							]

							lowerCAmelCase :					int            =      [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]

							lowerCAmelCase :					Optional[Any]            =      [
							    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
							]
							lowerCAmelCase :					List[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
							]
							lowerCAmelCase :					List[str]            =      [
							    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
							]
							lowerCAmelCase :					Optional[Any]            =      [
							    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
							]
							lowerCAmelCase :					List[str]            =      [
							    -0.0_492, -0.0_524, -0.0_565, -0.0_640, -0.0_686, -0.0_684, -0.0_677, -0.0_664,
							    -0.0_605, -0.0_490, -0.0_330, -0.0_168, -0.0_083, -0.0_075, -0.0_051, 0.0_025,
							    0.0_136, 0.0_261, 0.0_386, 0.0_497, 0.0_580, 0.0_599, 0.0_583, 0.0_614,
							    0.0_740, 0.0_889, 0.1_023, 0.1_162, 0.1_211, 0.1_212, 0.1_251, 0.1_336,
							    0.1_502, 0.1_686, 0.1_883, 0.2_148, 0.2_363, 0.2_458, 0.2_507, 0.2_531
							]
							lowerCAmelCase :					Union[str, Any]            =      [
							    0.0_256, -0.0_544, 0.1_600, -0.0_032, 0.1_066, 0.0_825, -0.0_013, 0.3_440,
							    0.0_210, 0.0_412, -0.1_777, -0.0_892, -0.0_164, 0.0_285, -0.0_613, -0.0_617,
							    -0.0_137, -0.0_201, -0.0_175, 0.0_215, -0.0_627, 0.0_520, -0.0_730, 0.0_970,
							    -0.0_100, 0.0_442, -0.0_586, 0.0_207, -0.0_015, -0.0_082
							]
							lowerCAmelCase :					Optional[int]            =      [
							    0.0_415, 0.0_877, 0.0_022, -0.0_055, 0.0_751, 0.0_334, 0.0_324, -0.0_068,
							    0.0_011, 0.0_017, -0.0_676, 0.0_655, -0.0_143, 0.0_399, 0.0_303, 0.0_743,
							    -0.0_168, -0.0_394, -0.1_113, 0.0_124, 0.0_442, 0.0_267, -0.0_003, -0.1_536,
							    -0.0_116, -0.1_837, -0.0_180, -0.1_026, -0.0_777, -0.0_456
							]
							lowerCAmelCase :					Tuple            =      [
							    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 							__lowercase    (							self      : List[Any]	):
									_a  :   Optional[Any]				 =							JukeboxTokenizer.from_pretrained(self.model_id	)
									_a  :   Dict				 =							tokenizer(**self.metas	)['input_ids']
									return tokens







							@slow
							def 							__lowercase    (							self      : Optional[int]	):
									_a  :   List[str]				 =							JukeboxModel.from_pretrained(self.model_id			,min_duration=0	).eval()
									_a  :   List[str]				 =							self.prepare_inputs()

									set_seed(0	)
									_a  :   Tuple				 =							[torch.zeros(1			,0			,dtype=torch.long	).cpu() for _ in range(3	)]
									_a  :   Optional[int]				 =							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  :   Any				 =							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  :   Dict				 =							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 							__lowercase    (							self      : Union[str, Any]	):
									_a  :   Union[str, Any]				 =							False
									_a  :   List[Any]				 =							JukeboxModel.from_pretrained(self.model_id			,min_duration=0	).eval()

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

									_a  :   int				 =							model.priors[0]
									_a  :   int				 =							0
									_a  :   Optional[int]				 =							top_prior.get_music_tokens_conds(_UpperCAmelCase			,start=_UpperCAmelCase			,end=start + top_prior.n_ctx	)
									_a  :   Dict				 =							top_prior.get_metadata(labels[0].clone()			,_UpperCAmelCase			,1058304			,0	)

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

									_a ,      _a ,      _a  :   str				 =							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 							__lowercase    (							self      : Dict	):
									_a  :   int				 =							False

									_a  :   Optional[Any]				 =							JukeboxModel.from_pretrained(self.model_id			,min_duration=0	).eval()
									set_seed(0	)
									_a  :   List[str]				 =							torch.rand((1, 5120, 1)	)
									_a  :   Union[str, Any]				 =							list(self.prepare_inputs()	)

									_a  :   Tuple				 =							[model.vqvae.encode(_UpperCAmelCase			,start_level=2			,bs_chunks=waveform.shape[0]	)[0], None, None]
									_a  :   str				 =							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  :   str				 =							torch.cat((zs[0], torch.zeros(1			,2048 - zs[0].shape[-1]	))			,dim=-1	).long()
									_a  :   Union[str, Any]				 =							[upper_a, model.vqvae.encode(_UpperCAmelCase			,start_level=1			,bs_chunks=waveform.shape[0]	)[0], None]
									_a  :   Tuple				 =							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  :   List[Any]				 =							torch.cat((zs[1], torch.zeros(1			,2048 - zs[1].shape[-1]	))			,dim=-1	).long()
									_a  :   Tuple				 =							[upper_a, upper_a, model.vqvae.encode(_UpperCAmelCase			,start_level=0			,bs_chunks=waveform.shape[0]	)[0]]
									_a  :   Any				 =							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 							__lowercase    (							self      : Optional[int]	):
									_a  :   Optional[Any]				 =							JukeboxModel.from_pretrained(self.model_id			,min_duration=0	).eval()
									set_seed(0	)
									_a  :   List[Any]				 =							torch.rand((1, 5120, 1)	)
									with torch.no_grad():
											_a  :   Tuple				 =							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  :   Optional[Any]				 =							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 			__magic_name__		(					unittest.TestCase     ):
							lowerCAmelCase :					Union[str, Any]            =      (JukeboxModel,) if is_torch_available() else ()
							lowerCAmelCase :					List[str]            =      'openai/jukebox-5b-lyrics'
							lowerCAmelCase :					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
							lowerCAmelCase :					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,
							    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
							]
							lowerCAmelCase :					List[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, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
							    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
							    6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3
							]

							lowerCAmelCase :					Optional[int]            =      [
							    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
							]
							lowerCAmelCase :					List[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
							]

							lowerCAmelCase :					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
							]
							lowerCAmelCase :					Tuple            =      [
							    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
							]

							lowerCAmelCase :					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
							]
							lowerCAmelCase :					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
							]

							lowerCAmelCase :					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
							]
							lowerCAmelCase :					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 							__lowercase    (							self      : List[Any]			,_UpperCAmelCase      : Tuple	):
									_a  :   Any				 =							JukeboxTokenizer.from_pretrained(_UpperCAmelCase	)
									_a  :   int				 =							tokenizer(**self.metas	)['input_ids']
									return tokens







							@slow
							def 							__lowercase    (							self      : int	):
									_a  :   Optional[int]				 =							JukeboxModel.from_pretrained(self.model_id			,min_duration=0	).eval()
									_a  :   List[Any]				 =							self.prepare_inputs(self.model_id	)

									set_seed(0	)
									_a  :   int				 =							[torch.zeros(1			,0			,dtype=torch.long	).cpu() for _ in range(3	)]
									_a  :   int				 =							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  :   str				 =							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  :   int				 =							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 							__lowercase    (							self      : Tuple	):
									_a  :   Dict				 =							JukeboxModel.from_pretrained(self.model_id			,min_duration=0	).eval()
									_a  :   int				 =							[i.cuda() for i in self.prepare_inputs(self.model_id	)]

									set_seed(0	)
									model.priors[0].cuda()
									_a  :   Optional[Any]				 =							[torch.zeros(1			,0			,dtype=torch.long	).cuda() for _ in range(3	)]
									_a  :   Union[str, Any]				 =							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  :   str				 =							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  :   List[Any]				 =							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 							__lowercase    (							self      : Optional[int]	):
									_a  :   List[str]				 =							'ArthurZ/jukebox_prior_0'
									_a  :   List[str]				 =							JukeboxPrior.from_pretrained(_UpperCAmelCase			,min_duration=0	).eval().half().to('cuda'	)

									_a  :   Optional[int]				 =							self.prepare_inputs(_UpperCAmelCase	)[0].cuda()
									_a  :   List[str]				 =							model.get_metadata(_UpperCAmelCase			,0			,7680			,0	)
									set_seed(0	)
									_a  :   Tuple				 =							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 numpy as np
from matplotlib import pyplot as plt



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   Any:
		return 1 / (1 + np.exp(-1 * x						))

class 			__magic_name__		:







							def __init__(							self      : List[Any]			,_UpperCAmelCase      : List[Any]			,_UpperCAmelCase      : Any=None			,_UpperCAmelCase      : Union[str, Any]=None			,_UpperCAmelCase      : List[str]=False	):
									_a  :   Dict				 =							units
									_a  :   List[Any]				 =							None
									_a  :   str				 =							None
									_a  :   List[str]				 =							activation
									if learning_rate is None:
											_a  :   str				 =							0.3
									_a  :   Any				 =							learning_rate
									_a  :   int				 =							is_input_layer







							def 							__lowercase    (							self      : List[str]			,_UpperCAmelCase      : List[Any]	):
									_a  :   List[str]				 =							np.asmatrix(np.random.normal(0			,0.5			,(self.units, back_units)	)	)
									_a  :   int				 =							np.asmatrix(np.random.normal(0			,0.5			,self.units	)	).T
									if self.activation is None:
											_a  :   Tuple				 =							sigmoid







							def 							__lowercase    (							self      : Tuple	):
									# activation function may be sigmoid or linear
									if self.activation == sigmoid:
											_a  :   Dict				 =							np.dot(self.output			,(1 - self.output).T	)
											_a  :   str				 =							np.diag(np.diag(_UpperCAmelCase	)	)
									else:
											_a  :   int				 =							1
									return gradient_activation







							def 							__lowercase    (							self      : int			,_UpperCAmelCase      : Union[str, Any]	):
									_a  :   Union[str, Any]				 =							xdata
									if self.is_input_layer:
											# input layer
											_a  :   Optional[int]				 =							xdata
											_a  :   str				 =							xdata
											return xdata
									else:
											_a  :   Tuple				 =							np.dot(self.weight			,self.xdata	) - self.bias
											_a  :   Any				 =							self.activation(self.wx_plus_b	)
											return self.output







							def 							__lowercase    (							self      : str			,_UpperCAmelCase      : List[str]	):
									_a  :   Any				 =							self.cal_gradient()  # i * i 维
									_a  :   Optional[Any]				 =							np.asmatrix(np.dot(gradient.T			,_UpperCAmelCase	)	)

									_a  :   List[str]				 =							np.asmatrix(self.xdata	)
									_a  :   str				 =							-1
									_a  :   str				 =							self.weight

									_a  :   Tuple				 =							np.dot(gradient.T			,self._gradient_weight.T	)
									_a  :   List[str]				 =							gradient * self._gradient_bias
									_a  :   List[Any]				 =							np.dot(_UpperCAmelCase			,self._gradient_x	).T
									# upgrade: the Negative gradient direction
									_a  :   List[Any]				 =							self.weight - self.learn_rate * self.gradient_weight
									_a  :   int				 =							self.bias - self.learn_rate * self.gradient_bias.T
									# updates the weights and bias according to learning rate (0.3 if undefined)
									return self.gradient

class 			__magic_name__		:







							def __init__(							self      : str	):
									_a  :   Dict				 =							[]
									_a  :   List[Any]				 =							[]
									_a  :   Optional[Any]				 =							plt.figure()
									_a  :   List[Any]				 =							self.fig_loss.add_subplot(1			,1			,1	)







							def 							__lowercase    (							self      : Optional[int]			,_UpperCAmelCase      : str	):
									self.layers.append(_UpperCAmelCase	)







							def 							__lowercase    (							self      : List[Any]	):
									for i, layer in enumerate(self.layers[:]	):
											if i < 1:
													_a  :   Union[str, Any]				 =							True
											else:
													layer.initializer(self.layers[i - 1].units	)







							def 							__lowercase    (							self      : Any	):
									for i, layer in enumerate(self.layers[:]	):
											print(F"""------- layer {i} -------"""	)
											print('weight.shape '			,np.shape(layer.weight	)	)
											print('bias.shape '			,np.shape(layer.bias	)	)







							def 							__lowercase    (							self      : str			,_UpperCAmelCase      : str			,_UpperCAmelCase      : str			,_UpperCAmelCase      : List[Any]			,_UpperCAmelCase      : Optional[Any]	):
									_a  :   Union[str, Any]				 =							train_round
									_a  :   Optional[Any]				 =							accuracy

									self.ax_loss.hlines(self.accuracy			,0			,self.train_round * 1.1	)

									_a  :   List[Any]				 =							np.shape(_UpperCAmelCase	)
									for _ in range(_UpperCAmelCase	):
											_a  :   str				 =							0
											for row in range(x_shape[0]	):
													_a  :   List[str]				 =							np.asmatrix(xdata[row, :]	).T
													_a  :   List[str]				 =							np.asmatrix(ydata[row, :]	).T

													# forward propagation
													for layer in self.layers:
															_a  :   str				 =							layer.forward_propagation(_xdata	)

													_a ,      _a  :   Optional[int]				 =							self.cal_loss(_ydata			,_xdata	)
													_a  :   Optional[Any]				 =							all_loss + loss

													# back propagation: the input_layer does not upgrade
													for layer in self.layers[:0:-1]:
															_a  :   Optional[Any]				 =							layer.back_propagation(_UpperCAmelCase	)

											_a  :   str				 =							all_loss / x_shape[0]
											self.train_mse.append(_UpperCAmelCase	)

											self.plot_loss()

											if mse < self.accuracy:
													print('----达到精度----'	)
													return mse
									return None







							def 							__lowercase    (							self      : int			,_UpperCAmelCase      : str			,_UpperCAmelCase      : List[str]	):
									_a  :   Union[str, Any]				 =							np.sum(np.power((ydata - ydata_)			,2	)	)
									_a  :   Optional[Any]				 =							2 * (ydata_ - ydata)
									# vector (shape is the same as _ydata.shape)
									return self.loss, self.loss_gradient







							def 							__lowercase    (							self      : Optional[Any]	):
									if self.ax_loss.lines:
											self.ax_loss.lines.remove(self.ax_loss.lines[0]	)
									self.ax_loss.plot(self.train_mse			,'r-'	)
									plt.ion()
									plt.xlabel('step'	)
									plt.ylabel('loss'	)
									plt.show()
									plt.pause(0.1	)



def   __lowerCamelCase  (   )    ->   List[str]:
		_a  :   Union[str, Any]				 =							np.random.randn(10		,    10						)
		_a  :   str				 =							np.asarray(
		    [
		        [0.8, 0.4],
		        [0.4, 0.3],
		        [0.34, 0.45],
		        [0.67, 0.32],
		        [0.88, 0.67],
		        [0.78, 0.77],
		        [0.55, 0.66],
		        [0.55, 0.43],
		        [0.54, 0.1],
		        [0.1, 0.5],
		    ]						)
		_a  :   Tuple				 =							BPNN()
		for i in (10, 20, 30, 2):
				model.add_layer(DenseLayer(lowerCAmelCase_						)						)
		model.build()
		model.summary()
		model.train(xdata=lowerCAmelCase_		,    ydata=lowerCAmelCase_		,    train_round=100		,    accuracy=0.01						)


if __name__ == "__main__":
							example()




