





"""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 dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)



@flax.struct.dataclass
class      lowercase_				:
       snake_case__				:							CommonSchedulerState

       # setable values
       snake_case__				:							jnp.ndarray
       snake_case__				:							jnp.ndarray
       snake_case__				:							jnp.ndarray
       snake_case__				:							Optional[int]											=     None

       # running values
       snake_case__				:							Optional[jnp.ndarray]											=     None

       @classmethod
       def 		_SCREAMING_SNAKE_CASE(			cls   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        return cls(common=__SCREAMING_SNAKE_CASE   ,					init_noise_sigma=__SCREAMING_SNAKE_CASE   ,					timesteps=__SCREAMING_SNAKE_CASE   ,					sigmas=__SCREAMING_SNAKE_CASE     )



@dataclass
class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							LMSDiscreteSchedulerState



class      lowercase_				(     __lowerCAmelCase				,__lowerCAmelCase							):
       snake_case__				:							Any											=     [e.name for e in FlaxKarrasDiffusionSchedulers]

       snake_case__				:							jnp.dtype

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        return True

       @register_to_config
       def __init__(			self   ,					__SCREAMING_SNAKE_CASE = 1000   ,					__SCREAMING_SNAKE_CASE = 0.0001   ,					__SCREAMING_SNAKE_CASE = 0.02   ,					__SCREAMING_SNAKE_CASE = "linear"   ,					__SCREAMING_SNAKE_CASE = None   ,					__SCREAMING_SNAKE_CASE = "epsilon"   ,					__SCREAMING_SNAKE_CASE = jnp.floataa   ,					):






        '''simple docstring'''

        snake_case__    							=				dtype

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE = None     ):






        '''simple docstring'''

        if common is None:
         snake_case__    							=				CommonSchedulerState.create(self     )

        snake_case__    							=				jnp.arange(0   ,					self.config.num_train_timesteps     ).round()[::-1]
        snake_case__    							=				((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

        # standard deviation of the initial noise distribution
        snake_case__    							=				sigmas.max()

        return LMSDiscreteSchedulerState.create(
            common=__SCREAMING_SNAKE_CASE   ,					init_noise_sigma=__SCREAMING_SNAKE_CASE   ,					timesteps=__SCREAMING_SNAKE_CASE   ,					sigmas=__SCREAMING_SNAKE_CASE   ,					)

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        ((snake_case__)							,   )    							=				jnp.where(state.timesteps == timestep   ,					size=1     )
        snake_case__    							=				step_index[0]

        snake_case__    							=				state.sigmas[step_index]
        snake_case__    							=				sample / ((sigma**2 + 1) ** 0.5)
        return sample

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        def lms_derivative(__SCREAMING_SNAKE_CASE     ):
         snake_case__    							=				1.0
         for k in range(__SCREAMING_SNAKE_CASE     ):
          if current_order == k:
           continue
          prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
         return prod

        snake_case__    							=				integrate.quad(__SCREAMING_SNAKE_CASE   ,					state.sigmas[t]   ,					state.sigmas[t + 1]   ,					epsrel=1e-4     )[0]

        return integrated_coeff

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = ()     ):






        '''simple docstring'''

        snake_case__    							=				jnp.linspace(self.config.num_train_timesteps - 1   ,					0   ,					__SCREAMING_SNAKE_CASE   ,					dtype=self.dtype     )

        snake_case__    							=				jnp.floor(__SCREAMING_SNAKE_CASE     ).astype(jnp.intaa     )
        snake_case__    							=				jnp.ceil(__SCREAMING_SNAKE_CASE     ).astype(jnp.intaa     )

        snake_case__    							=				jnp.mod(__SCREAMING_SNAKE_CASE   ,					1.0     )

        snake_case__    							=				((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
        snake_case__    							=				(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
        snake_case__    							=				jnp.concatenate([sigmas, jnp.array([0.0]   ,					dtype=self.dtype     )]     )

        snake_case__    							=				timesteps.astype(jnp.intaa     )

        # initial running values
        snake_case__    							=				jnp.zeros((0,) + shape   ,					dtype=self.dtype     )

        return state.replace(
            timesteps=__SCREAMING_SNAKE_CASE   ,					sigmas=__SCREAMING_SNAKE_CASE   ,					num_inference_steps=__SCREAMING_SNAKE_CASE   ,					derivatives=__SCREAMING_SNAKE_CASE   ,					)

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = 4   ,					__SCREAMING_SNAKE_CASE = True   ,					):






        '''simple docstring'''

        if state.num_inference_steps is None:
         raise ValueError(
             "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"     )

        snake_case__    							=				state.sigmas[timestep]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
         snake_case__    							=				sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
         # * c_out + input * c_skip
         snake_case__    							=				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        else:
         raise ValueError(
             F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"     )

        # 2. Convert to an ODE derivative
        snake_case__    							=				(sample - pred_original_sample) / sigma
        snake_case__    							=				state.replace(derivatives=jnp.append(state.derivatives   ,					__SCREAMING_SNAKE_CASE     )     )
        if len(state.derivatives     ) > order:
         snake_case__    							=				state.replace(derivatives=jnp.delete(state.derivatives   ,					0     )     )

        # 3. Compute linear multistep coefficients
        snake_case__    							=				min(timestep + 1   ,					__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				[self.get_lms_coefficient(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ) for curr_order in range(__SCREAMING_SNAKE_CASE     )]

        # 4. Compute previous sample based on the derivatives path
        snake_case__    							=				sample + sum(
            coeff * derivative for coeff, derivative in zip(__SCREAMING_SNAKE_CASE   ,					reversed(state.derivatives     )     )     )

        if not return_dict:
         return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE   ,					state=__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

        snake_case__    							=				state.sigmas[timesteps].flatten()
        snake_case__    							=				broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE   ,					noise.shape     )

        snake_case__    							=				original_samples + noise * sigma

        return noisy_samples

       def __len__(			self     ):






        '''simple docstring'''

        return self.config.num_train_timesteps

