





import unittest
from unittest import skip

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow
from transformers.trainer_utils import set_seed


if is_torch_available():
 import torch

 from transformers import JukeboxModel, JukeboxPrior, JukeboxTokenizer


@require_torch
class 					__magic_name__  (							unittest.TestCase							):





     """simple docstring"""



     __UpperCamelCase					    =     (JukeboxModel,) if is_torch_available() else ()
     __UpperCamelCase					    =     '''openai/jukebox-1b-lyrics'''
     __UpperCamelCase					    =     {
         '''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
     __UpperCamelCase					    =     [
         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
     ]

     __UpperCamelCase					    =     [
         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
     ]

     __UpperCamelCase					    =     [
         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
     ]
     __UpperCamelCase					    =     [
         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
     ]

     __UpperCamelCase					    =     [
         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
     ]
     __UpperCamelCase					    =     [
         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
     ]

     __UpperCamelCase					    =     [1_05_83_04, 0, 78_64_32, 71_69, 5_07, 76, 27, 40, 30, 76]

     __UpperCamelCase					    =     [
         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
     ]
     __UpperCamelCase					    =     [
         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
     ]
     __UpperCamelCase					    =     [
         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
     ]
     __UpperCamelCase					    =     [
         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
     ]
     __UpperCamelCase					    =     [
         -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
     ]
     __UpperCamelCase					    =     [
         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
     ]
     __UpperCamelCase					    =     [
         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
     ]
     __UpperCamelCase					    =     [
         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       :List[str]  ):





        '''simple docstring'''




        A_					:				int          =			JukeboxTokenizer.from_pretrained(self.model_id  )
        A_					:				List[str]          =			tokenizer(**self.metas  )["input_ids"]
        return tokens



     @slow
     def 						SCREAMING_SNAKE_CASE		(							self       :str  ):





        '''simple docstring'''




        A_					:				Tuple          =			JukeboxModel.from_pretrained(self.model_id			,      min_duration=0  ).eval()
        A_					:				Optional[int]          =			self.prepare_inputs()

        set_seed(0  )
        A_					:				Any          =			[torch.zeros(1			,      0			,      dtype=torch.long  ).cpu() for _ in range(3  )]
        A_					:				Tuple          =			model._sample(snake_case			,      snake_case			,      [0]			,      sample_length=40 * model.priors[0].raw_to_tokens			,      save_results=snake_case  )
        self.assertIn(zs[0][0].detach().cpu().tolist()			,      [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]  )

        set_seed(0  )
        A_					:				Optional[int]          =			model._sample(snake_case			,      snake_case			,      [1]			,      sample_length=40 * model.priors[1].raw_to_tokens			,      save_results=snake_case  )
        self.assertIn(zs[1][0].detach().cpu().tolist()			,      [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]  )

        set_seed(0  )
        A_					:				int          =			model._sample(snake_case			,      snake_case			,      [2]			,      sample_length=40 * model.priors[2].raw_to_tokens			,      save_results=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       :Optional[Any]  ):





        '''simple docstring'''




        A_					:				Dict          =			False
        A_					:				Dict          =			JukeboxModel.from_pretrained(self.model_id			,      min_duration=0  ).eval()

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

        A_					:				int          =			model.priors[0]
        A_					:				Dict          =			0
        A_					:				List[Any]          =			top_prior.get_music_tokens_conds(snake_case			,      start=snake_case			,      end=start + top_prior.n_ctx  )
        A_					:				Tuple          =			top_prior.get_metadata(labels[0].clone()			,      snake_case			,      1_058_304			,      0  )

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

        A_      ,						A_      ,						A_					:				Dict          =			top_prior.get_cond(snake_case			,      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       :Union[str, Any]  ):





        '''simple docstring'''




        A_					:				Union[str, Any]          =			False

        A_					:				List[str]          =			JukeboxModel.from_pretrained(self.model_id			,      min_duration=0  ).eval()
        set_seed(0  )
        A_					:				Union[str, Any]          =			torch.rand((1, 5_120, 1)  )
        A_					:				Tuple          =			list(self.prepare_inputs()  )

        A_					:				Optional[int]          =			[model.vqvae.encode(snake_case			,      start_level=2			,      bs_chunks=waveform.shape[0]  )[0], None, None]
        A_					:				Dict          =			model._sample(
            snake_case			,      snake_case			,      sample_levels=[0]			,      save_results=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  )  )

        A_					:				Optional[Any]          =			torch.cat((zs[0], torch.zeros(1			,      2_048 - zs[0].shape[-1]  ))			,      dim=-1  ).long()
        A_					:				Union[str, Any]          =			[upper_a, model.vqvae.encode(snake_case			,      start_level=1			,      bs_chunks=waveform.shape[0]  )[0], None]
        A_					:				Any          =			model._sample(
            snake_case			,      snake_case			,      sample_levels=[1]			,      save_results=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  )  )

        A_					:				Any          =			torch.cat((zs[1], torch.zeros(1			,      2_048 - zs[1].shape[-1]  ))			,      dim=-1  ).long()
        A_					:				Optional[Any]          =			[upper_a, upper_a, model.vqvae.encode(snake_case			,      start_level=0			,      bs_chunks=waveform.shape[0]  )[0]]
        A_					:				str          =			model._sample(
            snake_case			,      snake_case			,      sample_levels=[2]			,      save_results=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       :List[str]  ):





        '''simple docstring'''




        A_					:				List[str]          =			JukeboxModel.from_pretrained(self.model_id			,      min_duration=0  ).eval()
        set_seed(0  )
        A_					:				Tuple          =			torch.rand((1, 5_120, 1)  )
        with torch.no_grad():
           A_					:				int          =			model.vqvae.encode(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():
           A_					:				str          =			model.vqvae.decode(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 					__magic_name__  (							unittest.TestCase							):





     """simple docstring"""



     __UpperCamelCase					    =     (JukeboxModel,) if is_torch_available() else ()
     __UpperCamelCase					    =     '''openai/jukebox-5b-lyrics'''
     __UpperCamelCase					    =     {
         '''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
     __UpperCamelCase					    =     [
         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
     ]
     __UpperCamelCase					    =     [
         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
     ]

     __UpperCamelCase					    =     [
         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
     ]
     __UpperCamelCase					    =     [
         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
     ]

     __UpperCamelCase					    =     [
         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
     ]
     __UpperCamelCase					    =     [
         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
     ]

     __UpperCamelCase					    =     [
         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
     ]
     __UpperCamelCase					    =     [
         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
     ]

     __UpperCamelCase					    =     [
         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
     ]
     __UpperCamelCase					    =     [
         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       :List[str]			,      snake_case       :Any  ):





        '''simple docstring'''




        A_					:				Union[str, Any]          =			JukeboxTokenizer.from_pretrained(snake_case  )
        A_					:				Union[str, Any]          =			tokenizer(**self.metas  )["input_ids"]
        return tokens



     @slow
     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        A_					:				Optional[Any]          =			JukeboxModel.from_pretrained(self.model_id			,      min_duration=0  ).eval()
        A_					:				int          =			self.prepare_inputs(self.model_id  )

        set_seed(0  )
        A_					:				str          =			[torch.zeros(1			,      0			,      dtype=torch.long  ).cpu() for _ in range(3  )]
        A_					:				List[Any]          =			model._sample(snake_case			,      snake_case			,      [0]			,      sample_length=60 * model.priors[0].raw_to_tokens			,      save_results=snake_case  )
        self.assertIn(zs[0][0].detach().cpu().tolist()			,      [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]  )

        set_seed(0  )
        A_					:				Dict          =			model._sample(snake_case			,      snake_case			,      [1]			,      sample_length=60 * model.priors[1].raw_to_tokens			,      save_results=snake_case  )
        self.assertIn(zs[1][0].detach().cpu().tolist()			,      [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]  )

        set_seed(0  )
        A_					:				Tuple          =			model._sample(snake_case			,      snake_case			,      [2]			,      sample_length=60 * model.priors[2].raw_to_tokens			,      save_results=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       :int  ):





        '''simple docstring'''




        A_					:				Union[str, Any]          =			JukeboxModel.from_pretrained(self.model_id			,      min_duration=0  ).eval()
        A_					:				Any          =			[i.cuda() for i in self.prepare_inputs(self.model_id  )]

        set_seed(0  )
        model.priors[0].cuda()
        A_					:				int          =			[torch.zeros(1			,      0			,      dtype=torch.long  ).cuda() for _ in range(3  )]
        A_					:				Dict          =			model._sample(snake_case			,      snake_case			,      [0]			,      sample_length=60 * model.priors[0].raw_to_tokens			,      save_results=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()
        A_					:				List[str]          =			model._sample(snake_case			,      snake_case			,      [1]			,      sample_length=60 * model.priors[1].raw_to_tokens			,      save_results=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()
        A_					:				Optional[Any]          =			model._sample(snake_case			,      snake_case			,      [2]			,      sample_length=60 * model.priors[2].raw_to_tokens			,      save_results=snake_case  )
        torch.testing.assert_allclose(zs[2][0].cpu()			,      torch.tensor(self.EXPECTED_GPU_OUTPUTS_0  )  )





     @slow
     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        A_					:				Tuple          =			"ArthurZ/jukebox_prior_0"
        A_					:				Any          =			JukeboxPrior.from_pretrained(snake_case			,      min_duration=0  ).eval().half().to("cuda"  )

        A_					:				Union[str, Any]          =			self.prepare_inputs(snake_case  )[0].cuda()
        A_					:				Any          =			model.get_metadata(snake_case			,      0			,      7_680			,      0  )
        set_seed(0  )
        A_					:				Optional[Any]          =			model.sample(1			,      metadata=snake_case			,      sample_tokens=60  )
        self.assertIn(outputs[0].cpu().tolist()			,      [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]  )







import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()

def      __snake_case						(					_lowerCAmelCase : Optional[int]				,				_lowerCAmelCase : List[str]				,				_lowerCAmelCase : List[Any]	) ->						Any:
   # Initialise PyTorch model
   A_					:				Tuple          =			FNetConfig.from_json_file(_lowerCAmelCase	)
   print(f"Building PyTorch model from configuration: {config}"	)
   A_					:				Tuple          =			FNetForPreTraining(_lowerCAmelCase	)

   A_					:				List[str]          =			restore_checkpoint(_lowerCAmelCase				,				_lowerCAmelCase	)
   A_					:				Any          =			checkpoint_dict["target"]

   # Embeddings
   # Position IDs
   A_					:				Optional[Any]          =			fnet_pretraining_model.state_dict()

   A_					:				Optional[int]          =			state_dict["fnet.embeddings.position_ids"]
   A_					:				Union[str, Any]          =			{"fnet.embeddings.position_ids": position_ids}
   # Embedding Layers
   A_					:				str          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]	)
   A_					:				List[Any]          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]	)
   A_					:				int          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]	)
   A_					:				List[Any]          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]	).T
   A_					:				Dict          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]	)
   A_					:				int          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]	)
   A_					:				Any          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]	)

   # Encoder Layers
   for layer in range(config.num_hidden_layers	):
      A_					:				List[str]          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["mixing_layer_norm"]["scale"]	)
      A_					:				Union[str, Any]          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["mixing_layer_norm"]["bias"]	)

      A_					:				Union[str, Any]          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["kernel"]	).T
      A_					:				List[Any]          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["intermediate"]["bias"]	)

      A_					:				Optional[int]          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["kernel"]	).T
      A_					:				int          =			torch.tensor(
          pretrained_model_params["encoder"][f"feed_forward_{layer}"]["output"]["bias"]	)

      A_					:				Any          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["output_layer_norm"]["scale"]	)
      A_					:				Tuple          =			torch.tensor(
          pretrained_model_params["encoder"][f"encoder_{layer}"]["output_layer_norm"]["bias"]	)

   # Pooler Layers
   A_					:				Optional[Any]          =			torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]	).T
   A_					:				List[Any]          =			torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]	)

   # Masked LM Layers
   A_					:				Optional[Any]          =			torch.tensor(
       pretrained_model_params["predictions_dense"]["kernel"]	).T
   A_					:				int          =			torch.tensor(
       pretrained_model_params["predictions_dense"]["bias"]	)
   A_					:				str          =			torch.tensor(
       pretrained_model_params["predictions_layer_norm"]["scale"]	)
   A_					:				List[Any]          =			torch.tensor(
       pretrained_model_params["predictions_layer_norm"]["bias"]	)
   A_					:				Tuple          =			torch.tensor(
       pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]	)
   A_					:				Any          =			torch.tensor(
       pretrained_model_params["predictions_output"]["output_bias"]	)
   A_					:				List[str]          =			torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]	)

   # Seq Relationship Layers
   A_					:				str          =			torch.tensor(
       pretrained_model_params["classification"]["output_kernel"]	)
   A_					:				str          =			torch.tensor(
       pretrained_model_params["classification"]["output_bias"]	)

   # Load State Dict
   fnet_pretraining_model.load_state_dict(_lowerCAmelCase	)

   # Save PreTrained
   print(f"Saving pretrained model to {save_path}"	)
   fnet_pretraining_model.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
 _lowerCAmelCase							:      Tuple						=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--fnet_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained FNet model. \n'''
         '''This specifies the model architecture.'''
     ),
 )
 parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
 _lowerCAmelCase							:      Optional[int]						=	parser.parse_args()
 convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)
