code: 
  
  
  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        lowercase_				( unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		(JukeboxModel,) if is_torch_available() else ()
  							snake_case_        =		'''openai/jukebox-1b-lyrics'''
  							snake_case_        =		{
  							    '''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
  							snake_case_        =		[
  							    18_64, 15_36, 12_13, 18_70, 13_57, 15_36, 5_19, 8_80, 13_23, 7_89, 10_82, 5_34,
  							    10_00, 14_45, 11_05, 11_30, 9_67, 5_15, 14_34, 16_20, 5_34, 14_95, 2_83, 14_45,
  							    3_33, 13_07, 5_39, 16_31, 15_28, 3_75, 14_34, 6_73, 6_27, 7_10, 7_78, 18_83,
  							    14_05, 12_76, 14_55, 12_28
  							]
  
  							snake_case_        =		[
  							    14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
  							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
  							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
  							    6_53, 6_53, 6_53, 6_53
  							]
  
  							snake_case_        =		[
  							    11_25, 17_51, 6_97, 17_76, 11_41, 14_76, 3_91, 6_97, 11_25, 6_84, 8_67, 4_16,
  							    8_44, 13_72, 12_74, 7_17, 12_74, 8_44, 12_99, 14_19, 6_97, 13_70, 3_17, 11_25,
  							    1_91, 14_40, 13_70, 14_40, 13_70, 2_82, 16_21, 13_70, 3_68, 3_49, 8_67, 18_72,
  							    12_62, 8_69, 17_28, 7_47
  							]
  							snake_case_        =		[
  							    4_16, 4_16, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
  							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
  							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
  							    4_16, 4_16, 4_16, 4_16
  							]
  
  							snake_case_        =		[
  							    17_55, 8_42, 3_07, 18_43, 10_22, 13_95, 2_34, 15_54, 8_06, 7_39, 10_22, 4_42,
  							    6_16, 5_56, 2_68, 14_99, 9_33, 4_57, 14_40, 18_37, 7_55, 9_85, 3_08, 9_02,
  							    2_93, 14_43, 16_71, 11_41, 15_33, 5_55, 15_62, 10_61, 2_87, 4_17, 10_22, 20_08,
  							    11_86, 10_15, 17_77, 2_68
  							]
  							snake_case_        =		[
  							    8_54, 8_42, 13_53, 1_14, 13_53, 8_42, 1_85, 8_42, 1_85, 1_14, 5_91, 8_42,
  							    1_85, 4_17, 1_85, 8_42, 3_07, 8_42, 5_91, 8_42, 1_85, 8_42, 3_07, 8_42,
  							    5_91, 8_42, 13_53, 8_42, 1_85, 8_42, 5_91, 8_42, 5_91, 1_14, 5_91, 8_42,
  							    1_85, 8_42, 5_91, 89
  							]
  
  							snake_case_        =		[1_05_83_04, 0, 78_64_32, 71_69, 5_07, 76, 27, 40, 30, 76]
  
  							snake_case_        =		[
  							    3_90, 11_60, 10_02, 19_07, 17_88, 17_88, 17_88, 19_07, 10_02, 10_02, 18_54, 10_02,
  							    10_02, 10_02, 10_02, 10_02, 10_02, 11_60, 11_60, 16_06, 5_96, 5_96, 11_60, 10_02,
  							    15_16, 5_96, 10_02, 10_02, 10_02, 19_07, 17_88, 17_88, 17_88, 18_54, 17_88, 19_07,
  							    19_07, 17_88, 5_96, 16_26
  							]
  							snake_case_        =		[
  							    12_36, 16_68, 14_84, 19_20, 18_48, 14_09, 1_39, 8_64, 18_28, 12_72, 15_99, 8_24,
  							    16_72, 1_39, 5_55, 14_84, 8_24, 19_20, 5_55, 5_96, 15_79, 15_99, 12_31, 15_99,
  							    16_37, 14_07, 2_12, 8_24, 15_99, 1_16, 14_33, 8_24, 2_58, 15_99, 14_33, 18_95,
  							    10_63, 14_33, 14_33, 15_99
  							]
  							snake_case_        =		[
  							    16_84, 18_73, 11_19, 11_89, 3_95, 6_11, 19_01, 9_72, 8_90, 13_37, 13_92, 19_27,
  							    96, 9_72, 6_72, 7_80, 11_19, 8_90, 1_58, 7_71, 10_73, 19_27, 3_53, 13_31,
  							    12_69, 14_59, 13_33, 16_45, 8_12, 15_77, 13_37, 6_06, 3_53, 9_81, 14_66, 6_19,
  							    1_97, 3_91, 3_02, 19_30
  							]
  							snake_case_        =		[
  							    3_90, 11_60, 10_02, 19_07, 17_88, 17_88, 17_88, 19_07, 10_02, 10_02, 18_54, 10_02,
  							    10_02, 10_02, 10_02, 10_02, 10_02, 11_60, 11_60, 16_06, 5_96, 5_96, 11_60, 10_02,
  							    15_16, 5_96, 10_02, 10_02, 10_02, 19_07, 17_88, 17_88, 17_88, 18_54, 17_88, 19_07,
  							    19_07, 17_88, 5_96, 16_26
  							]
  							snake_case_        =		[
  							    -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
  							]
  							snake_case_        =		[
  							    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
  							]
  							snake_case_        =		[
  							    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
  							]
  							snake_case_        =		[
  							    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 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		JukeboxTokenizer.from_pretrained(self.model_id		)
  													a_					=		tokenizer(**self.metas		)["""input_ids"""]
  													return tokens
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													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 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													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_058_304			,    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 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		False
  
  													a_					=		JukeboxModel.from_pretrained(self.model_id			,    min_duration=0		).eval()
  													set_seed(0		)
  													a_					=		torch.rand((1, 5_120, 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			,    2_048 - 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			,    2_048 - 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 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		JukeboxModel.from_pretrained(self.model_id			,    min_duration=0		).eval()
  													set_seed(0		)
  													a_					=		torch.rand((1, 5_120, 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        lowercase_				( unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		(JukeboxModel,) if is_torch_available() else ()
  							snake_case_        =		'''openai/jukebox-5b-lyrics'''
  							snake_case_        =		{
  							    '''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
  							snake_case_        =		[
  							    14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
  							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
  							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
  							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
  							    14_89, 14_89, 14_89, 14_89, 11_50, 18_53, 15_09, 11_50, 13_57, 15_09, 6, 12_72
  							]
  							snake_case_        =		[
  							    14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
  							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
  							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
  							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
  							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53
  							]
  
  							snake_case_        =		[
  							    11_25, 4_16, 11_25, 11_25, 11_25, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16,
  							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
  							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
  							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
  							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
  							]
  							snake_case_        =		[
  							    4_16, 4_16, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
  							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
  							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
  							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
  							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
  							]
  
  							snake_case_        =		[
  							    17_55, 10_61, 2_34, 17_55, 10_61, 17_55, 1_85, 2_90, 3_07, 3_07, 6_16, 6_16,
  							    6_16, 6_16, 6_16, 6_16, 3_07, 2_90, 4_17, 17_55, 2_34, 17_55, 1_85, 2_90,
  							    2_90, 2_90, 3_07, 6_16, 6_16, 6_16, 6_16, 6_16, 2_90, 2_34, 2_34, 17_55,
  							    2_34, 2_34, 17_55, 2_34, 1_85, 1_85, 3_07, 6_16, 6_16, 6_16, 6_16, 2_90,
  							    17_55, 17_55, 17_55, 2_34, 2_34, 17_55, 15_72, 2_90, 3_07, 6_16, 34, 6_16
  							]
  							snake_case_        =		[
  							    8_54, 8_42, 13_53, 1_14, 13_53, 8_42, 1_85, 8_42, 1_85, 1_14, 5_91, 8_42, 1_85,
  							    4_17, 1_85, 8_42, 3_07, 8_42, 5_91, 8_42, 1_85, 8_42, 1_85, 8_42, 5_91, 8_42,
  							    13_53, 8_42, 1_85, 8_42, 5_91, 8_42, 5_91, 1_14, 5_91, 8_42, 1_85, 8_42, 5_91,
  							    89, 5_91, 8_42, 5_91, 8_42, 5_91, 4_17, 13_72, 8_42, 13_72, 8_42, 34, 8_42,
  							    1_85, 89, 5_91, 8_42, 1_85, 8_42, 5_91, 6_32
  							]
  
  							snake_case_        =		[
  							    14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
  							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
  							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
  							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
  							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53
  							]
  							snake_case_        =		[
  							    14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
  							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
  							    6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 18_53, 11_77, 15_36, 12_28,
  							    7_10, 4_75, 14_89, 12_29, 12_24, 2_31, 12_24, 2_52, 14_34, 6_53, 4_75,
  							    11_06, 18_77, 15_99, 12_28, 16_00, 16_83, 11_82, 18_53, 4_75, 18_64,
  							    2_52, 12_29, 14_34, 20_01
  							]
  
  							snake_case_        =		[
  							    11_25, 11_25, 4_16, 11_25, 11_25, 4_16, 11_25, 11_25, 4_16, 4_16, 11_25, 4_16,
  							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
  							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
  							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
  							    4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
  							]
  							snake_case_        =		[
  							    4_91, 17_55, 34, 16_13, 17_55, 4_17, 9_92, 16_13, 2_22, 8_42, 13_53, 16_13,
  							    8_44, 6_32, 1_85, 16_13, 8_44, 6_32, 1_85, 16_13, 1_85, 8_42, 6_77, 16_13,
  							    1_85, 1_14, 13_53, 16_13, 3_07, 89, 8_44, 16_13, 3_07, 13_32, 2_34, 19_79,
  							    3_07, 89, 13_53, 6_16, 34, 8_42, 1_85, 8_42, 34, 8_42, 1_85, 8_42,
  							    3_07, 1_14, 1_85, 89, 34, 12_68, 1_85, 89, 34, 8_42, 1_85, 89
  							]
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		JukeboxTokenizer.from_pretrained(_UpperCAmelCase		)
  													a_					=		tokenizer(**self.metas		)["""input_ids"""]
  													return tokens
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													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 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													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 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													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			,    7_680			,    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]		)
style_context: 
  
  
  import unittest
  
  import numpy as np
  import timeout_decorator  # noqa
  
  from transformers import MBartConfig, is_flax_available
  from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
  from transformers.utils import cached_property
  
  from ...generation.test_flax_utils import FlaxGenerationTesterMixin
  from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
  
  
  if is_flax_available():
  	import os
  
  	# The slow tests are often failing with OOM error on GPU
  	# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
  	# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
  	A_    :					List[Any]             ="""platform"""
  
  	import jax
  	import jax.numpy as jnp
  
  	from transformers import AutoTokenizer
  	from transformers.models.mbart.modeling_flax_mbart import (
  	    FlaxMBartForConditionalGeneration,
  	    FlaxMBartForQuestionAnswering,
  	    FlaxMBartForSequenceClassification,
  	    FlaxMBartModel,
  	    shift_tokens_right,
  	)
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						if attention_mask is None:
  												a_					=		np.where(input_ids != config.pad_token_id       ,    1       ,    0      )
  						if decoder_attention_mask is None:
  												a_					=		np.where(decoder_input_ids != config.pad_token_id       ,    1       ,    0      )
  						if head_mask is None:
  												a_					=		np.ones((config.encoder_layers, config.encoder_attention_heads)      )
  						if decoder_head_mask is None:
  												a_					=		np.ones((config.decoder_layers, config.decoder_attention_heads)      )
  						if cross_attn_head_mask is None:
  												a_					=		np.ones((config.decoder_layers, config.decoder_attention_heads)      )
  						return {
  						    "input_ids": input_ids,
  						    "decoder_input_ids": decoder_input_ids,
  						    "attention_mask": attention_mask,
  						    "decoder_attention_mask": decoder_attention_mask,
  						}
  
  
  
  
  
  class        lowercase_				( unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def __init__(   self			,    _UpperCAmelCase			,    _UpperCAmelCase=13			,    _UpperCAmelCase=7			,    _UpperCAmelCase=True			,    _UpperCAmelCase=False			,    _UpperCAmelCase=99			,    _UpperCAmelCase=16			,    _UpperCAmelCase=2			,    _UpperCAmelCase=4			,    _UpperCAmelCase=4			,    _UpperCAmelCase="gelu"			,    _UpperCAmelCase=0.1			,    _UpperCAmelCase=0.1			,    _UpperCAmelCase=32			,    _UpperCAmelCase=2			,    _UpperCAmelCase=1			,    _UpperCAmelCase=0			,    _UpperCAmelCase=2			,    _UpperCAmelCase=0.0_2			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		parent
  													a_					=		batch_size
  													a_					=		seq_length
  													a_					=		is_training
  													a_					=		use_labels
  													a_					=		vocab_size
  													a_					=		hidden_size
  													a_					=		num_hidden_layers
  													a_					=		num_attention_heads
  													a_					=		intermediate_size
  													a_					=		hidden_act
  													a_					=		hidden_dropout_prob
  													a_					=		attention_probs_dropout_prob
  													a_					=		max_position_embeddings
  													a_					=		eos_token_id
  													a_					=		pad_token_id
  													a_					=		bos_token_id
  													a_					=		decoder_start_token_id
  													a_					=		initializer_range
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,    self.vocab_size		)			,    3			,    self.vocab_size		)
  													a_					=		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,    dtype=np.intaa		))			,    -1		)
  
  													a_					=		shift_tokens_right(_UpperCAmelCase			,    1		)
  
  													a_					=		MBartConfig(
  													    vocab_size=self.vocab_size			,    d_model=self.hidden_size			,    encoder_layers=self.num_hidden_layers			,    decoder_layers=self.num_hidden_layers			,    encoder_attention_heads=self.num_attention_heads			,    decoder_attention_heads=self.num_attention_heads			,    encoder_ffn_dim=self.intermediate_size			,    decoder_ffn_dim=self.intermediate_size			,    dropout=self.hidden_dropout_prob			,    attention_dropout=self.attention_probs_dropout_prob			,    max_position_embeddings=self.max_position_embeddings			,    eos_token_id=self.eos_token_id			,    bos_token_id=self.bos_token_id			,    pad_token_id=self.pad_token_id			,    decoder_start_token_id=self.decoder_start_token_id			,    initializer_range=self.initializer_range			,    use_cache=_UpperCAmelCase			,    )
  													a_					=		prepare_mbart_inputs_dict(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  													return config, inputs_dict
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_			,   a_					=		self.prepare_config_and_inputs()
  													return config, inputs_dict
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		20
  													a_					=		model_class_name(_UpperCAmelCase		)
  
  													a_					=		model.encode(inputs_dict["""input_ids"""]		)
  
  													a_			,   a_					=		(
  													    inputs_dict["""decoder_input_ids"""],
  													    inputs_dict["""decoder_attention_mask"""],
  													)
  
  													a_					=		model.init_cache(decoder_input_ids.shape[0]			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  													a_					=		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,    dtype="""i4"""		)
  
  													a_					=		jnp.broadcast_to(
  													    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]			,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,    )
  													a_					=		model.decode(
  													    decoder_input_ids[:, :-1]			,    _UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase			,    past_key_values=_UpperCAmelCase			,    decoder_position_ids=_UpperCAmelCase			,    )
  
  													a_					=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,    dtype="""i4"""		)
  													a_					=		model.decode(
  													    decoder_input_ids[:, -1:]			,    _UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase			,    past_key_values=outputs_cache.past_key_values			,    decoder_position_ids=_UpperCAmelCase			,    )
  
  													a_					=		model.decode(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  													a_					=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
  													self.parent.assertTrue(diff < 1e-3			,    msg=f"Max diff is {diff}"		)
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		20
  													a_					=		model_class_name(_UpperCAmelCase		)
  
  													a_					=		model.encode(inputs_dict["""input_ids"""]		)
  
  													a_			,   a_					=		(
  													    inputs_dict["""decoder_input_ids"""],
  													    inputs_dict["""decoder_attention_mask"""],
  													)
  
  													a_					=		jnp.concatenate(
  													    [
  													        decoder_attention_mask,
  													        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
  													    ]			,    axis=-1			,    )
  
  													a_					=		model.init_cache(decoder_input_ids.shape[0]			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  													a_					=		jnp.broadcast_to(
  													    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]			,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,    )
  
  													a_					=		model.decode(
  													    decoder_input_ids[:, :-1]			,    _UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase			,    past_key_values=_UpperCAmelCase			,    decoder_position_ids=_UpperCAmelCase			,    )
  													a_					=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,    dtype="""i4"""		)
  													a_					=		model.decode(
  													    decoder_input_ids[:, -1:]			,    _UpperCAmelCase			,    past_key_values=outputs_cache.past_key_values			,    decoder_attention_mask=_UpperCAmelCase			,    decoder_position_ids=_UpperCAmelCase			,    )
  
  													a_					=		model.decode(_UpperCAmelCase			,    _UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase		)
  
  													a_					=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
  													self.parent.assertTrue(diff < 1e-3			,    msg=f"Max diff is {diff}"		)
  
  
  
  
  
  @require_flax
  class        lowercase_				( unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		99
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		np.array(
  													    [
  													        [71, 82, 18, 33, 46, 91, 2],
  													        [68, 34, 26, 58, 30, 82, 2],
  													        [5, 97, 17, 39, 94, 40, 2],
  													        [76, 83, 94, 25, 70, 78, 2],
  													        [87, 59, 41, 35, 48, 66, 2],
  													        [55, 13, 16, 58, 5, 2, 1],  # note padding
  													        [64, 27, 31, 51, 12, 75, 2],
  													        [52, 64, 86, 17, 83, 39, 2],
  													        [48, 61, 9, 24, 71, 82, 2],
  													        [26, 1, 60, 48, 22, 13, 2],
  													        [21, 5, 62, 28, 14, 76, 2],
  													        [45, 98, 37, 86, 59, 48, 2],
  													        [70, 70, 50, 9, 28, 0, 2],
  													    ]			,    dtype=np.intaa			,    )
  
  													a_					=		input_ids.shape[0]
  													a_					=		MBartConfig(
  													    vocab_size=self.vocab_size			,    d_model=24			,    encoder_layers=2			,    decoder_layers=2			,    encoder_attention_heads=2			,    decoder_attention_heads=2			,    encoder_ffn_dim=32			,    decoder_ffn_dim=32			,    max_position_embeddings=48			,    eos_token_id=2			,    pad_token_id=1			,    bos_token_id=0			,    )
  													return config, input_ids, batch_size
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_			,   a_			,   a_					=		self._get_config_and_data()
  													a_					=		FlaxMBartForSequenceClassification(_UpperCAmelCase		)
  													a_					=		model(input_ids=_UpperCAmelCase			,    decoder_input_ids=_UpperCAmelCase		)
  													a_					=		(batch_size, config.num_labels)
  													self.assertEqual(outputs["""logits"""].shape			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_			,   a_			,   a_					=		self._get_config_and_data()
  													a_					=		FlaxMBartForQuestionAnswering(_UpperCAmelCase		)
  													a_					=		model(input_ids=_UpperCAmelCase		)
  
  													self.assertEqual(outputs["""start_logits"""].shape			,    input_ids.shape		)
  													self.assertEqual(outputs["""end_logits"""].shape			,    input_ids.shape		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_			,   a_			,   a_					=		self._get_config_and_data()
  													a_					=		FlaxMBartForConditionalGeneration(_UpperCAmelCase		)
  													a_					=		lm_model(input_ids=_UpperCAmelCase		)
  													a_					=		(batch_size, input_ids.shape[1], config.vocab_size)
  													self.assertEqual(outputs["""logits"""].shape			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		MBartConfig(
  													    vocab_size=self.vocab_size			,    d_model=14			,    encoder_layers=2			,    decoder_layers=2			,    encoder_attention_heads=2			,    decoder_attention_heads=2			,    encoder_ffn_dim=8			,    decoder_ffn_dim=8			,    max_position_embeddings=48			,    )
  													a_					=		FlaxMBartForConditionalGeneration(_UpperCAmelCase		)
  													a_					=		np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]			,    dtype=np.intaa		)
  													a_					=		np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]			,    dtype=np.intaa		)
  													a_					=		lm_model(input_ids=_UpperCAmelCase			,    decoder_input_ids=_UpperCAmelCase		)
  													a_					=		(*summary.shape, config.vocab_size)
  													self.assertEqual(outputs["""logits"""].shape			,    _UpperCAmelCase		)
  
  
  
  
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]			,    dtype=np.intaa		)
  													a_					=		shift_tokens_right(_UpperCAmelCase			,    1		)
  													a_					=		np.equal(_UpperCAmelCase			,    1		).astype(np.floataa		).sum()
  													a_					=		np.equal(_UpperCAmelCase			,    1		).astype(np.floataa		).sum()
  													self.assertEqual(shifted.shape			,    input_ids.shape		)
  													self.assertEqual(_UpperCAmelCase			,    n_pad_before - 1		)
  													self.assertTrue(np.equal(shifted[:, 0]			,    2		).all()		)
  
  
  
  
  
  @require_flax
  class        lowercase_				( UpperCamelCase__			,unittest.TestCase			,UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		True
  							snake_case_        =		(
  							    (
  							        FlaxMBartModel,
  							        FlaxMBartForConditionalGeneration,
  							        FlaxMBartForSequenceClassification,
  							        FlaxMBartForQuestionAnswering,
  							    )
  							    if is_flax_available()
  							    else ()
  							)
  							snake_case_        =		(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		FlaxMBartModelTester(self		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_			,   a_					=		self.model_tester.prepare_config_and_inputs()
  													for model_class in self.all_model_classes:
  																			self.model_tester.check_use_cache_forward(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_			,   a_					=		self.model_tester.prepare_config_and_inputs()
  													for model_class in self.all_model_classes:
  																			self.model_tester.check_use_cache_forward_with_attn_mask(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_			,   a_					=		self.model_tester.prepare_config_and_inputs_for_common()
  
  													for model_class in self.all_model_classes:
  																			with self.subTest(model_class.__name__		):
  																									a_					=		self._prepare_for_class(_UpperCAmelCase			,    _UpperCAmelCase		)
  																									a_					=		model_class(_UpperCAmelCase		)
  
  																									@jax.jit
  																									def encode_jitted(_UpperCAmelCase			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):
  																															return model.encode(input_ids=_UpperCAmelCase			,    attention_mask=_UpperCAmelCase		)
  
  																									with self.subTest("""JIT Enabled"""		):
  																															a_					=		encode_jitted(**_UpperCAmelCase		).to_tuple()
  
  																									with self.subTest("""JIT Disabled"""		):
  																															with jax.disable_jit():
  																																					a_					=		encode_jitted(**_UpperCAmelCase		).to_tuple()
  
  																									self.assertEqual(len(_UpperCAmelCase		)			,    len(_UpperCAmelCase		)		)
  																									for jitted_output, output in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
  																															self.assertEqual(jitted_output.shape			,    output.shape		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_			,   a_					=		self.model_tester.prepare_config_and_inputs_for_common()
  
  													for model_class in self.all_model_classes:
  																			with self.subTest(model_class.__name__		):
  																									a_					=		model_class(_UpperCAmelCase		)
  																									a_					=		model.encode(inputs_dict["""input_ids"""]			,    inputs_dict["""attention_mask"""]		)
  
  																									a_					=		{
  																									    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
  																									    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
  																									    """encoder_outputs""": encoder_outputs,
  																									}
  
  																									@jax.jit
  																									def decode_jitted(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  																															return model.decode(
  																															    decoder_input_ids=_UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase			,    encoder_outputs=_UpperCAmelCase			,    )
  
  																									with self.subTest("""JIT Enabled"""		):
  																															a_					=		decode_jitted(**_UpperCAmelCase		).to_tuple()
  
  																									with self.subTest("""JIT Disabled"""		):
  																															with jax.disable_jit():
  																																					a_					=		decode_jitted(**_UpperCAmelCase		).to_tuple()
  
  																									self.assertEqual(len(_UpperCAmelCase		)			,    len(_UpperCAmelCase		)		)
  																									for jitted_output, output in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
  																															self.assertEqual(jitted_output.shape			,    output.shape		)
  
  
  
  
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for model_class_name in self.all_model_classes:
  																			a_					=		model_class_name.from_pretrained("""facebook/mbart-large-cc25"""			,    from_pt=_UpperCAmelCase		)
  																			# FlaxMBartForSequenceClassification expects eos token in input_ids
  																			a_					=		np.ones((1, 1)		) * model.config.eos_token_id
  																			a_					=		model(_UpperCAmelCase		)
  																			self.assertIsNotNone(_UpperCAmelCase		)
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class        lowercase_				( unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		[
  							    ''' UN Chief Says There Is No Military Solution in Syria''',
  							]
  							snake_case_        =		[
  							    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
  							]
  							snake_case_        =		'''facebook/mbart-large-en-ro'''
  
  
  							@cached_property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return AutoTokenizer.from_pretrained(self.model_name		)
  
  
  							@cached_property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		FlaxMBartForConditionalGeneration.from_pretrained(self.model_name			,    from_pt=_UpperCAmelCase		)
  													return model
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.translate_src_text(**_UpperCAmelCase		)
  													self.assertListEqual(self.expected_text			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.tokenizer(self.src_text			,    **_UpperCAmelCase			,    return_tensors="""np"""		)
  													a_					=		self.model.generate(
  													    model_inputs.input_ids			,    attention_mask=model_inputs.attention_mask			,    decoder_start_token_id=self.tokenizer.lang_code_to_id["""ro_RO"""]			,    early_stopping=_UpperCAmelCase			,    num_beams=2			,    ).sequences
  													a_					=		self.tokenizer.batch_decode(_UpperCAmelCase			,    skip_special_tokens=_UpperCAmelCase		)
  													return generated_words
  
  
  
  
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													self._assert_generated_batch_equal_expected()