





"""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      lowercase_				(     unittest.TestCase							):
       snake_case__				:							Dict											=     (JukeboxModel,) if is_torch_available() else ()
       snake_case__				:							int											=     """openai/jukebox-1b-lyrics"""
       snake_case__				:							Union[str, Any]											=     {
           """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__				:							Any											=     [
           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__				:							Any											=     [
           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__				:							str											=     [
           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__				:							Optional[int]											=     [
           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__				:							Optional[Any]											=     [
           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__				:							Any											=     [
           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__				:							Union[str, Any]											=     [1_05_83_04, 0, 78_64_32, 71_69, 5_07, 76, 27, 40, 30, 76]

       snake_case__				:							Tuple											=     [
           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__				:							Any											=     [
           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__				:							Union[str, Any]											=     [
           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__				:							List[Any]											=     [
           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__				:							Optional[Any]											=     [
           -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__				:							str											=     [
           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__				:							Tuple											=     [
           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__				:							List[str]											=     [
           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 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

       @slow
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        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(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					[0]   ,					sample_length=40 * model.priors[0].raw_to_tokens   ,					save_results=__SCREAMING_SNAKE_CASE     )
        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(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					[1]   ,					sample_length=40 * model.priors[1].raw_to_tokens   ,					save_results=__SCREAMING_SNAKE_CASE     )
        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(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					[2]   ,					sample_length=40 * model.priors[2].raw_to_tokens   ,					save_results=__SCREAMING_SNAKE_CASE     )
        self.assertIn(zs[2][0].detach().cpu().tolist()   ,					[self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]     )

       @slow
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        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(__SCREAMING_SNAKE_CASE   ,					start=__SCREAMING_SNAKE_CASE   ,					end=start + top_prior.n_ctx     )
        snake_case__    							=				top_prior.get_metadata(labels[0].clone()   ,					__SCREAMING_SNAKE_CASE   ,					105_8304   ,					0     )

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

        snake_case__							,   snake_case__							,   snake_case__    							=				top_prior.get_cond(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )
        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 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				False

        snake_case__    							=				JukeboxModel.from_pretrained(self.model_id   ,					min_duration=0     ).eval()
        set_seed(0     )
        snake_case__    							=				torch.rand((1, 5120, 1)     )
        snake_case__    							=				list(self.prepare_inputs()     )

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

        snake_case__    							=				torch.cat((zs[0], torch.zeros(1   ,					2048 - zs[0].shape[-1]     ))   ,					dim=-1     ).long()
        snake_case__    							=				[upper_a, model.vqvae.encode(__SCREAMING_SNAKE_CASE   ,					start_level=1   ,					bs_chunks=waveform.shape[0]     )[0], None]
        snake_case__    							=				model._sample(
            __SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					sample_levels=[1]   ,					save_results=__SCREAMING_SNAKE_CASE   ,					sample_length=40 * model.priors[1].raw_to_tokens     )
        torch.testing.assert_allclose(zs[1][0][:40]   ,					torch.tensor(self.EXPECTED_PRIMED_1     )     )

        snake_case__    							=				torch.cat((zs[1], torch.zeros(1   ,					2048 - zs[1].shape[-1]     ))   ,					dim=-1     ).long()
        snake_case__    							=				[upper_a, upper_a, model.vqvae.encode(__SCREAMING_SNAKE_CASE   ,					start_level=0   ,					bs_chunks=waveform.shape[0]     )[0]]
        snake_case__    							=				model._sample(
            __SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					sample_levels=[2]   ,					save_results=__SCREAMING_SNAKE_CASE   ,					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 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				JukeboxModel.from_pretrained(self.model_id   ,					min_duration=0     ).eval()
        set_seed(0     )
        snake_case__    							=				torch.rand((1, 5120, 1)     )
        with torch.no_grad():
         snake_case__    							=				model.vqvae.encode(__SCREAMING_SNAKE_CASE   ,					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(__SCREAMING_SNAKE_CASE   ,					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							):
       snake_case__				:							Any											=     (JukeboxModel,) if is_torch_available() else ()
       snake_case__				:							str											=     """openai/jukebox-5b-lyrics"""
       snake_case__				:							Optional[Any]											=     {
           """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__				:							Tuple											=     [
           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__				:							str											=     [
           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__				:							Optional[Any]											=     [
           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__				:							List[Any]											=     [
           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__				:							int											=     [
           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__				:							Tuple											=     [
           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__				:							Dict											=     [
           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__				:							Tuple											=     [
           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__				:							Union[str, Any]											=     [
           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__				:							List[str]											=     [
           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 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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

       @slow
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        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(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					[0]   ,					sample_length=60 * model.priors[0].raw_to_tokens   ,					save_results=__SCREAMING_SNAKE_CASE     )
        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(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					[1]   ,					sample_length=60 * model.priors[1].raw_to_tokens   ,					save_results=__SCREAMING_SNAKE_CASE     )
        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(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					[2]   ,					sample_length=60 * model.priors[2].raw_to_tokens   ,					save_results=__SCREAMING_SNAKE_CASE     )
        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 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        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(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					[0]   ,					sample_length=60 * model.priors[0].raw_to_tokens   ,					save_results=__SCREAMING_SNAKE_CASE     )
        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(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					[1]   ,					sample_length=60 * model.priors[1].raw_to_tokens   ,					save_results=__SCREAMING_SNAKE_CASE     )
        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(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					[2]   ,					sample_length=60 * model.priors[2].raw_to_tokens   ,					save_results=__SCREAMING_SNAKE_CASE     )
        torch.testing.assert_allclose(zs[2][0].cpu()   ,					torch.tensor(self.EXPECTED_GPU_OUTPUTS_0     )     )

       @slow
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

        snake_case__    							=				self.prepare_inputs(__SCREAMING_SNAKE_CASE     )[0].cuda()
        snake_case__    							=				model.get_metadata(__SCREAMING_SNAKE_CASE   ,					0   ,					7680   ,					0     )
        set_seed(0     )
        snake_case__    							=				model.sample(1   ,					metadata=__SCREAMING_SNAKE_CASE   ,					sample_tokens=60     )
        self.assertIn(outputs[0].cpu().tolist()   ,					[self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]     )








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__					:     List[Any]							=				{
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     Any							=				["""BartTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     List[Any]							=				[
       """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BartForCausalLM""",
       """BartForConditionalGeneration""",
       """BartForQuestionAnswering""",
       """BartForSequenceClassification""",
       """BartModel""",
       """BartPretrainedModel""",
       """PretrainedBartModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     List[str]							=				[
       """TFBartForConditionalGeneration""",
       """TFBartForSequenceClassification""",
       """TFBartModel""",
       """TFBartPretrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     Any							=				[
       """FlaxBartDecoderPreTrainedModel""",
       """FlaxBartForCausalLM""",
       """FlaxBartForConditionalGeneration""",
       """FlaxBartForQuestionAnswering""",
       """FlaxBartForSequenceClassification""",
       """FlaxBartModel""",
       """FlaxBartPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
   from .tokenization_bart import BartTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bart_fast import BartTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bart import (
          BART_PRETRAINED_MODEL_ARCHIVE_LIST,
          BartForCausalLM,
          BartForConditionalGeneration,
          BartForQuestionAnswering,
          BartForSequenceClassification,
          BartModel,
          BartPretrainedModel,
          PretrainedBartModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_bart import (
          TFBartForConditionalGeneration,
          TFBartForSequenceClassification,
          TFBartModel,
          TFBartPretrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_bart import (
          FlaxBartDecoderPreTrainedModel,
          FlaxBartForCausalLM,
          FlaxBartForConditionalGeneration,
          FlaxBartForQuestionAnswering,
          FlaxBartForSequenceClassification,
          FlaxBartModel,
          FlaxBartPreTrainedModel,
      )

else:
   import sys

   a__					:     int							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

