code: 
  
  
  
  """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 			UpperCAmelCase_						(       unittest.TestCase		):
        __SCREAMING_SNAKE_CASE		:     List[Any]				  =				(JukeboxModel,) if is_torch_available() else ()
        __SCREAMING_SNAKE_CASE		:     Optional[Any]				  =				'openai/jukebox-1b-lyrics'
        __SCREAMING_SNAKE_CASE		:     List[str]				  =				{
            'artist': 'Zac Brown Band',
            'genres': 'Country',
            'lyrics': 'I met a traveller from an antique land,\n    Who said "Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ',
        }
        # fmt: off
        __SCREAMING_SNAKE_CASE		:     List[str]				  =				[
            1_8_6_4, 1_5_3_6, 1_2_1_3, 1_8_7_0, 1_3_5_7, 1_5_3_6, 5_1_9, 8_8_0, 1_3_2_3, 7_8_9, 1_0_8_2, 5_3_4,
            1_0_0_0, 1_4_4_5, 1_1_0_5, 1_1_3_0, 9_6_7, 5_1_5, 1_4_3_4, 1_6_2_0, 5_3_4, 1_4_9_5, 2_8_3, 1_4_4_5,
            3_3_3, 1_3_0_7, 5_3_9, 1_6_3_1, 1_5_2_8, 3_7_5, 1_4_3_4, 6_7_3, 6_2_7, 7_1_0, 7_7_8, 1_8_8_3,
            1_4_0_5, 1_2_7_6, 1_4_5_5, 1_2_2_8
        ]
  
        __SCREAMING_SNAKE_CASE		:     Tuple				  =				[
            1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
            6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
            6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
            6_5_3, 6_5_3, 6_5_3, 6_5_3
        ]
  
        __SCREAMING_SNAKE_CASE		:     Dict				  =				[
            1_1_2_5, 1_7_5_1, 6_9_7, 1_7_7_6, 1_1_4_1, 1_4_7_6, 3_9_1, 6_9_7, 1_1_2_5, 6_8_4, 8_6_7, 4_1_6,
            8_4_4, 1_3_7_2, 1_2_7_4, 7_1_7, 1_2_7_4, 8_4_4, 1_2_9_9, 1_4_1_9, 6_9_7, 1_3_7_0, 3_1_7, 1_1_2_5,
            1_9_1, 1_4_4_0, 1_3_7_0, 1_4_4_0, 1_3_7_0, 2_8_2, 1_6_2_1, 1_3_7_0, 3_6_8, 3_4_9, 8_6_7, 1_8_7_2,
            1_2_6_2, 8_6_9, 1_7_2_8, 7_4_7
        ]
        __SCREAMING_SNAKE_CASE		:     Dict				  =				[
            4_1_6, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
            4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
            4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
            4_1_6, 4_1_6, 4_1_6, 4_1_6
        ]
  
        __SCREAMING_SNAKE_CASE		:     Union[str, Any]				  =				[
            1_7_5_5, 8_4_2, 3_0_7, 1_8_4_3, 1_0_2_2, 1_3_9_5, 2_3_4, 1_5_5_4, 8_0_6, 7_3_9, 1_0_2_2, 4_4_2,
            6_1_6, 5_5_6, 2_6_8, 1_4_9_9, 9_3_3, 4_5_7, 1_4_4_0, 1_8_3_7, 7_5_5, 9_8_5, 3_0_8, 9_0_2,
            2_9_3, 1_4_4_3, 1_6_7_1, 1_1_4_1, 1_5_3_3, 5_5_5, 1_5_6_2, 1_0_6_1, 2_8_7, 4_1_7, 1_0_2_2, 2_0_0_8,
            1_1_8_6, 1_0_1_5, 1_7_7_7, 2_6_8
        ]
        __SCREAMING_SNAKE_CASE		:     Any				  =				[
            8_5_4, 8_4_2, 1_3_5_3, 1_1_4, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 1_1_4, 5_9_1, 8_4_2,
            1_8_5, 4_1_7, 1_8_5, 8_4_2, 3_0_7, 8_4_2, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 3_0_7, 8_4_2,
            5_9_1, 8_4_2, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 1_1_4, 5_9_1, 8_4_2,
            1_8_5, 8_4_2, 5_9_1, 8_9
        ]
  
        __SCREAMING_SNAKE_CASE		:     Any				  =				[1_0_5_8_3_0_4, 0, 7_8_6_4_3_2, 7_1_6_9, 5_0_7, 7_6, 2_7, 4_0, 3_0, 7_6]
  
        __SCREAMING_SNAKE_CASE		:     List[str]				  =				[
            3_9_0, 1_1_6_0, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_9_0_7, 1_0_0_2, 1_0_0_2, 1_8_5_4, 1_0_0_2,
            1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_1_6_0, 1_1_6_0, 1_6_0_6, 5_9_6, 5_9_6, 1_1_6_0, 1_0_0_2,
            1_5_1_6, 5_9_6, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_8_5_4, 1_7_8_8, 1_9_0_7,
            1_9_0_7, 1_7_8_8, 5_9_6, 1_6_2_6
        ]
        __SCREAMING_SNAKE_CASE		:     str				  =				[
            1_2_3_6, 1_6_6_8, 1_4_8_4, 1_9_2_0, 1_8_4_8, 1_4_0_9, 1_3_9, 8_6_4, 1_8_2_8, 1_2_7_2, 1_5_9_9, 8_2_4,
            1_6_7_2, 1_3_9, 5_5_5, 1_4_8_4, 8_2_4, 1_9_2_0, 5_5_5, 5_9_6, 1_5_7_9, 1_5_9_9, 1_2_3_1, 1_5_9_9,
            1_6_3_7, 1_4_0_7, 2_1_2, 8_2_4, 1_5_9_9, 1_1_6, 1_4_3_3, 8_2_4, 2_5_8, 1_5_9_9, 1_4_3_3, 1_8_9_5,
            1_0_6_3, 1_4_3_3, 1_4_3_3, 1_5_9_9
        ]
        __SCREAMING_SNAKE_CASE		:     Any				  =				[
            1_6_8_4, 1_8_7_3, 1_1_1_9, 1_1_8_9, 3_9_5, 6_1_1, 1_9_0_1, 9_7_2, 8_9_0, 1_3_3_7, 1_3_9_2, 1_9_2_7,
            9_6, 9_7_2, 6_7_2, 7_8_0, 1_1_1_9, 8_9_0, 1_5_8, 7_7_1, 1_0_7_3, 1_9_2_7, 3_5_3, 1_3_3_1,
            1_2_6_9, 1_4_5_9, 1_3_3_3, 1_6_4_5, 8_1_2, 1_5_7_7, 1_3_3_7, 6_0_6, 3_5_3, 9_8_1, 1_4_6_6, 6_1_9,
            1_9_7, 3_9_1, 3_0_2, 1_9_3_0
        ]
        __SCREAMING_SNAKE_CASE		:     Dict				  =				[
            3_9_0, 1_1_6_0, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_9_0_7, 1_0_0_2, 1_0_0_2, 1_8_5_4, 1_0_0_2,
            1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_1_6_0, 1_1_6_0, 1_6_0_6, 5_9_6, 5_9_6, 1_1_6_0, 1_0_0_2,
            1_5_1_6, 5_9_6, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_8_5_4, 1_7_8_8, 1_9_0_7,
            1_9_0_7, 1_7_8_8, 5_9_6, 1_6_2_6
        ]
        __SCREAMING_SNAKE_CASE		:     List[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
        ]
        __SCREAMING_SNAKE_CASE		:     List[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
        ]
        __SCREAMING_SNAKE_CASE		:     Union[str, Any]				  =				[
            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
        ]
        __SCREAMING_SNAKE_CASE		:     Tuple				  =				[
            7_6, 2_7, 4_0, 3_0, 7_6, 4_6, 4_4, 4_7, 4_0, 3_7, 3_8, 3_1, 4_5, 4_5, 7_6, 3_8, 3_1, 3_3,
            4_5, 7_6, 4_1, 3_2, 7_6, 4_5, 4_6, 4_1, 4_0, 3_1, 7_8, 7_6
        ]
  
  
  
        def 		snake_case_      (  self  :			Any		):
             _UpperCAmelCase    :			Any              =					JukeboxTokenizer.from_pretrained(self.model_id		)
             _UpperCAmelCase    :			List[str]              =					tokenizer(**self.metas		)["input_ids"]
             return tokens
  
  
  
        @slow
        def 		snake_case_      (  self  :			Optional[Any]		):
             _UpperCAmelCase    :			Any              =					JukeboxModel.from_pretrained(self.model_id  ,			min_duration=0		).eval()
             _UpperCAmelCase    :			int              =					self.prepare_inputs()
  
             set_seed(0		)
             _UpperCAmelCase    :			List[Any]              =					[torch.zeros(1  ,			0  ,			dtype=torch.long		).cpu() for _ in range(3		)]
             _UpperCAmelCase    :			Any              =					model._sample(A  ,			A  ,			[0]  ,			sample_length=4_0 * model.priors[0].raw_to_tokens  ,			save_results=A		)
             self.assertIn(zs[0][0].detach().cpu().tolist()  ,			[self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]		)
  
             set_seed(0		)
             _UpperCAmelCase    :			int              =					model._sample(A  ,			A  ,			[1]  ,			sample_length=4_0 * model.priors[1].raw_to_tokens  ,			save_results=A		)
             self.assertIn(zs[1][0].detach().cpu().tolist()  ,			[self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]		)
  
             set_seed(0		)
             _UpperCAmelCase    :			Tuple              =					model._sample(A  ,			A  ,			[2]  ,			sample_length=4_0 * model.priors[2].raw_to_tokens  ,			save_results=A		)
             self.assertIn(zs[2][0].detach().cpu().tolist()  ,			[self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]		)
  
  
  
        @slow
        def 		snake_case_      (  self  :			Dict		):
             _UpperCAmelCase    :			Optional[Any]              =					False
             _UpperCAmelCase    :			List[Any]              =					JukeboxModel.from_pretrained(self.model_id  ,			min_duration=0		).eval()
  
             _UpperCAmelCase    :			Optional[int]              =					self.prepare_inputs()
             set_seed(0		)
             _UpperCAmelCase    :			Optional[Any]              =					[torch.zeros(1  ,			0  ,			dtype=torch.long		) for _ in range(3		)]
  
             _UpperCAmelCase    :			str              =					model.priors[0]
             _UpperCAmelCase    :			Optional[Any]              =					0
             _UpperCAmelCase    :			List[str]              =					top_prior.get_music_tokens_conds(A  ,			start=A  ,			end=start + top_prior.n_ctx		)
             _UpperCAmelCase    :			int              =					top_prior.get_metadata(labels[0].clone()  ,			A  ,			1_0_5_8_3_0_4  ,			0		)
  
             self.assertIsNone(A		)
             self.assertListEqual(metadata.numpy()[0][:1_0].tolist()  ,			self.EXPECTED_Y_COND		)
  
             _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase    :			str              =					top_prior.get_cond(A  ,			A		)
             torch.testing.assert_allclose(
                 audio_conditioning[0][0][:3_0].detach()  ,			torch.tensor(self.EXPECTED_AUDIO_COND		)  ,			atol=1e-4  ,			rtol=1e-4		)
             torch.testing.assert_allclose(
                 metadata_conditioning[0][0][:3_0].detach()  ,			torch.tensor(self.EXPECTED_META_COND		)  ,			atol=1e-4  ,			rtol=1e-4		)
             torch.testing.assert_allclose(
                 lyric_tokens[0, :3_0].detach()  ,			torch.tensor(self.EXPECTED_LYRIC_COND		)  ,			atol=1e-4  ,			rtol=1e-4		)
  
  
  
        @slow
        def 		snake_case_      (  self  :			List[str]		):
             _UpperCAmelCase    :			str              =					False
  
             _UpperCAmelCase    :			Dict              =					JukeboxModel.from_pretrained(self.model_id  ,			min_duration=0		).eval()
             set_seed(0		)
             _UpperCAmelCase    :			str              =					torch.rand((1, 5_1_2_0, 1)		)
             _UpperCAmelCase    :			Any              =					list(self.prepare_inputs()		)
  
             _UpperCAmelCase    :			Optional[Any]              =					[model.vqvae.encode(A  ,			start_level=2  ,			bs_chunks=waveform.shape[0]		)[0], None, None]
             _UpperCAmelCase    :			Dict              =					model._sample(
                 A  ,			A  ,			sample_levels=[0]  ,			save_results=A  ,			sample_length=4_0 * model.priors[0].raw_to_tokens		)
             torch.testing.assert_allclose(zs[0][0][:4_0]  ,			torch.tensor(self.EXPECTED_PRIMED_0		)		)
  
             _UpperCAmelCase    :			int              =					torch.cat((zs[0], torch.zeros(1  ,			2_0_4_8 - zs[0].shape[-1]		))  ,			dim=-1		).long()
             _UpperCAmelCase    :			int              =					[upper_a, model.vqvae.encode(A  ,			start_level=1  ,			bs_chunks=waveform.shape[0]		)[0], None]
             _UpperCAmelCase    :			Tuple              =					model._sample(
                 A  ,			A  ,			sample_levels=[1]  ,			save_results=A  ,			sample_length=4_0 * model.priors[1].raw_to_tokens		)
             torch.testing.assert_allclose(zs[1][0][:4_0]  ,			torch.tensor(self.EXPECTED_PRIMED_1		)		)
  
             _UpperCAmelCase    :			str              =					torch.cat((zs[1], torch.zeros(1  ,			2_0_4_8 - zs[1].shape[-1]		))  ,			dim=-1		).long()
             _UpperCAmelCase    :			str              =					[upper_a, upper_a, model.vqvae.encode(A  ,			start_level=0  ,			bs_chunks=waveform.shape[0]		)[0]]
             _UpperCAmelCase    :			List[Any]              =					model._sample(
                 A  ,			A  ,			sample_levels=[2]  ,			save_results=A  ,			sample_length=4_0 * model.priors[2].raw_to_tokens		)
             torch.testing.assert_allclose(zs[2][0][:4_0].cpu()  ,			torch.tensor(self.EXPECTED_PRIMED_2		)		)
  
  
  
        @slow
        def 		snake_case_      (  self  :			int		):
             _UpperCAmelCase    :			Any              =					JukeboxModel.from_pretrained(self.model_id  ,			min_duration=0		).eval()
             set_seed(0		)
             _UpperCAmelCase    :			Tuple              =					torch.rand((1, 5_1_2_0, 1)		)
             with torch.no_grad():
                  _UpperCAmelCase    :			Any              =					model.vqvae.encode(A  ,			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():
                  _UpperCAmelCase    :			str              =					model.vqvae.decode(A  ,			start_level=2  ,			bs_chunks=x.shape[0]		)
             torch.testing.assert_allclose(x[0, :4_0, 0]  ,			torch.tensor(self.EXPECTED_VQVAE_DECODE		)  ,			atol=1e-4  ,			rtol=1e-4		)
  
  
  
  
  
  
  
  @require_torch
  class 			UpperCAmelCase_						(       unittest.TestCase		):
        __SCREAMING_SNAKE_CASE		:     str				  =				(JukeboxModel,) if is_torch_available() else ()
        __SCREAMING_SNAKE_CASE		:     str				  =				'openai/jukebox-5b-lyrics'
        __SCREAMING_SNAKE_CASE		:     Tuple				  =				{
            'artist': 'Zac Brown Band',
            'genres': 'Country',
            'lyrics': 'I met a traveller from an antique land,\n    Who said "Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ',
        }
  
        # fmt: off
        __SCREAMING_SNAKE_CASE		:     Optional[int]				  =				[
            1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
            6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
            6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
            6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
            1_4_8_9, 1_4_8_9, 1_4_8_9, 1_4_8_9, 1_1_5_0, 1_8_5_3, 1_5_0_9, 1_1_5_0, 1_3_5_7, 1_5_0_9, 6, 1_2_7_2
        ]
        __SCREAMING_SNAKE_CASE		:     List[Any]				  =				[
            1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
            6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
            6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
            6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
            6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3
        ]
  
        __SCREAMING_SNAKE_CASE		:     Optional[int]				  =				[
            1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 1_1_2_5, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
            4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
            4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
            4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
            4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
        ]
        __SCREAMING_SNAKE_CASE		:     Tuple				  =				[
            4_1_6, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
            4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
            4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
            4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
            4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
        ]
  
        __SCREAMING_SNAKE_CASE		:     List[Any]				  =				[
            1_7_5_5, 1_0_6_1, 2_3_4, 1_7_5_5, 1_0_6_1, 1_7_5_5, 1_8_5, 2_9_0, 3_0_7, 3_0_7, 6_1_6, 6_1_6,
            6_1_6, 6_1_6, 6_1_6, 6_1_6, 3_0_7, 2_9_0, 4_1_7, 1_7_5_5, 2_3_4, 1_7_5_5, 1_8_5, 2_9_0,
            2_9_0, 2_9_0, 3_0_7, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 2_9_0, 2_3_4, 2_3_4, 1_7_5_5,
            2_3_4, 2_3_4, 1_7_5_5, 2_3_4, 1_8_5, 1_8_5, 3_0_7, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 2_9_0,
            1_7_5_5, 1_7_5_5, 1_7_5_5, 2_3_4, 2_3_4, 1_7_5_5, 1_5_7_2, 2_9_0, 3_0_7, 6_1_6, 3_4, 6_1_6
        ]
        __SCREAMING_SNAKE_CASE		:     Union[str, Any]				  =				[
            8_5_4, 8_4_2, 1_3_5_3, 1_1_4, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 1_1_4, 5_9_1, 8_4_2, 1_8_5,
            4_1_7, 1_8_5, 8_4_2, 3_0_7, 8_4_2, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2,
            1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 1_1_4, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 5_9_1,
            8_9, 5_9_1, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 4_1_7, 1_3_7_2, 8_4_2, 1_3_7_2, 8_4_2, 3_4, 8_4_2,
            1_8_5, 8_9, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 6_3_2
        ]
  
        __SCREAMING_SNAKE_CASE		:     List[str]				  =				[
            1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
            6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
            6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
            6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
            6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3
        ]
        __SCREAMING_SNAKE_CASE		:     Tuple				  =				[
            1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
            6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
            6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 1_8_5_3, 1_1_7_7, 1_5_3_6, 1_2_2_8,
            7_1_0, 4_7_5, 1_4_8_9, 1_2_2_9, 1_2_2_4, 2_3_1, 1_2_2_4, 2_5_2, 1_4_3_4, 6_5_3, 4_7_5,
            1_1_0_6, 1_8_7_7, 1_5_9_9, 1_2_2_8, 1_6_0_0, 1_6_8_3, 1_1_8_2, 1_8_5_3, 4_7_5, 1_8_6_4,
            2_5_2, 1_2_2_9, 1_4_3_4, 2_0_0_1
        ]
  
        __SCREAMING_SNAKE_CASE		:     Optional[int]				  =				[
            1_1_2_5, 1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 1_1_2_5, 4_1_6,
            4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
            4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
            4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
            4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
        ]
        __SCREAMING_SNAKE_CASE		:     int				  =				[
            4_9_1, 1_7_5_5, 3_4, 1_6_1_3, 1_7_5_5, 4_1_7, 9_9_2, 1_6_1_3, 2_2_2, 8_4_2, 1_3_5_3, 1_6_1_3,
            8_4_4, 6_3_2, 1_8_5, 1_6_1_3, 8_4_4, 6_3_2, 1_8_5, 1_6_1_3, 1_8_5, 8_4_2, 6_7_7, 1_6_1_3,
            1_8_5, 1_1_4, 1_3_5_3, 1_6_1_3, 3_0_7, 8_9, 8_4_4, 1_6_1_3, 3_0_7, 1_3_3_2, 2_3_4, 1_9_7_9,
            3_0_7, 8_9, 1_3_5_3, 6_1_6, 3_4, 8_4_2, 1_8_5, 8_4_2, 3_4, 8_4_2, 1_8_5, 8_4_2,
            3_0_7, 1_1_4, 1_8_5, 8_9, 3_4, 1_2_6_8, 1_8_5, 8_9, 3_4, 8_4_2, 1_8_5, 8_9
        ]
  
  
  
        def 		snake_case_      (  self  :			Optional[int]  ,			A  :			str		):
             _UpperCAmelCase    :			str              =					JukeboxTokenizer.from_pretrained(A		)
             _UpperCAmelCase    :			int              =					tokenizer(**self.metas		)["input_ids"]
             return tokens
  
  
  
        @slow
        def 		snake_case_      (  self  :			str		):
             _UpperCAmelCase    :			Optional[int]              =					JukeboxModel.from_pretrained(self.model_id  ,			min_duration=0		).eval()
             _UpperCAmelCase    :			Optional[int]              =					self.prepare_inputs(self.model_id		)
  
             set_seed(0		)
             _UpperCAmelCase    :			List[Any]              =					[torch.zeros(1  ,			0  ,			dtype=torch.long		).cpu() for _ in range(3		)]
             _UpperCAmelCase    :			List[str]              =					model._sample(A  ,			A  ,			[0]  ,			sample_length=6_0 * model.priors[0].raw_to_tokens  ,			save_results=A		)
             self.assertIn(zs[0][0].detach().cpu().tolist()  ,			[self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]		)
  
             set_seed(0		)
             _UpperCAmelCase    :			Dict              =					model._sample(A  ,			A  ,			[1]  ,			sample_length=6_0 * model.priors[1].raw_to_tokens  ,			save_results=A		)
             self.assertIn(zs[1][0].detach().cpu().tolist()  ,			[self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]		)
  
             set_seed(0		)
             _UpperCAmelCase    :			Optional[Any]              =					model._sample(A  ,			A  ,			[2]  ,			sample_length=6_0 * model.priors[2].raw_to_tokens  ,			save_results=A		)
             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 		snake_case_      (  self  :			Any		):
             _UpperCAmelCase    :			Tuple              =					JukeboxModel.from_pretrained(self.model_id  ,			min_duration=0		).eval()
             _UpperCAmelCase    :			Dict              =					[i.cuda() for i in self.prepare_inputs(self.model_id		)]
  
             set_seed(0		)
             model.priors[0].cuda()
             _UpperCAmelCase    :			Optional[Any]              =					[torch.zeros(1  ,			0  ,			dtype=torch.long		).cuda() for _ in range(3		)]
             _UpperCAmelCase    :			Optional[int]              =					model._sample(A  ,			A  ,			[0]  ,			sample_length=6_0 * model.priors[0].raw_to_tokens  ,			save_results=A		)
             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()
             _UpperCAmelCase    :			Optional[Any]              =					model._sample(A  ,			A  ,			[1]  ,			sample_length=6_0 * model.priors[1].raw_to_tokens  ,			save_results=A		)
             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()
             _UpperCAmelCase    :			str              =					model._sample(A  ,			A  ,			[2]  ,			sample_length=6_0 * model.priors[2].raw_to_tokens  ,			save_results=A		)
             torch.testing.assert_allclose(zs[2][0].cpu()  ,			torch.tensor(self.EXPECTED_GPU_OUTPUTS_0		)		)
  
  
  
        @slow
        def 		snake_case_      (  self  :			Tuple		):
             _UpperCAmelCase    :			Any              =					"ArthurZ/jukebox_prior_0"
             _UpperCAmelCase    :			int              =					JukeboxPrior.from_pretrained(A  ,			min_duration=0		).eval().half().to("cuda"		)
  
             _UpperCAmelCase    :			List[Any]              =					self.prepare_inputs(A		)[0].cuda()
             _UpperCAmelCase    :			Dict              =					model.get_metadata(A  ,			0  ,			7_6_8_0  ,			0		)
             set_seed(0		)
             _UpperCAmelCase    :			Union[str, Any]              =					model.sample(1  ,			metadata=A  ,			sample_tokens=6_0		)
             self.assertIn(outputs[0].cpu().tolist()  ,			[self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]		)
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import json
  import re
  import string
  import sys
  from collections import Counter
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[int]       )						->     Union[str, Any]:
  
  
  
  
  
       '''simple docstring'''
  
       def remove_articles(SCREAMING_SNAKE_CASE__			:		Tuple       ):
            return re.sub(R"\b(a|an|the)\b"		,						" "		,						SCREAMING_SNAKE_CASE__       )
  
       def white_space_fix(SCREAMING_SNAKE_CASE__			:		Any       ):
            return " ".join(text.split()       )
  
       def remove_punc(SCREAMING_SNAKE_CASE__			:		Optional[int]       ):
            _UpperCAmelCase    :			List[str]              =					set(string.punctuation       )
            return "".join(ch for ch in text if ch not in exclude       )
  
       def lower(SCREAMING_SNAKE_CASE__			:		List[str]       ):
            return text.lower()
  
       return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__       )       )       )       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Union[str, Any]		,						SCREAMING_SNAKE_CASE__			:		str       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			List[Any]              =					normalize_answer(SCREAMING_SNAKE_CASE__       ).split()
       _UpperCAmelCase    :			Tuple              =					normalize_answer(SCREAMING_SNAKE_CASE__       ).split()
       _UpperCAmelCase    :			List[Any]              =					Counter(SCREAMING_SNAKE_CASE__       ) & Counter(SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			List[str]              =					sum(common.values()       )
       if num_same == 0:
            return 0
       _UpperCAmelCase    :			Union[str, Any]              =					1.0 * num_same / len(SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			Union[str, Any]              =					1.0 * num_same / len(SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			Union[str, Any]              =					(2 * precision * recall) / (precision + recall)
       return fa
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Union[str, Any]		,						SCREAMING_SNAKE_CASE__			:		Dict       )						->     str:
  
  
  
  
  
       '''simple docstring'''
  
       return normalize_answer(SCREAMING_SNAKE_CASE__       ) == normalize_answer(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]		,						SCREAMING_SNAKE_CASE__			:		Dict		,						SCREAMING_SNAKE_CASE__			:		Optional[Any]       )						->     str:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			int              =					[]
       for ground_truth in ground_truths:
            _UpperCAmelCase    :			Dict              =					metric_fn(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
            scores_for_ground_truths.append(SCREAMING_SNAKE_CASE__       )
       return max(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[int]		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Tuple:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			str              =					0
       _UpperCAmelCase    :			int              =					[]
       for passage in dataset:
            for qa in passage["qas"]:
                 total += 1
                 if qa["id"] not in predictions:
                      _UpperCAmelCase    :			List[Any]              =					f'Unanswered question {qa["id"]} will receive score 0.'
                      print(SCREAMING_SNAKE_CASE__		,						file=sys.stderr       )
                      continue
  
                 _UpperCAmelCase    :			Union[str, Any]              =					[x["text"] for x in qa["answers"]]
                 _UpperCAmelCase    :			Optional[int]              =					predictions[qa["id"]]
  
                 _UpperCAmelCase    :			int              =					metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
                 if int(_exact_match       ) == 1:
                      correct_ids.append(qa["id"]       )
                 exact_match += _exact_match
  
                 fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
  
       _UpperCAmelCase    :			List[str]              =					exact_match / total
       _UpperCAmelCase    :			str              =					fa / total
  
       return {"exact_match": exact_match, "f1": fa}, correct_ids
  
  
  if __name__ == "__main__":
   _lowerCAmelCase       :      Any							=							"1.0"
   _lowerCAmelCase       :      Any							=							argparse.ArgumentParser("Official evaluation script for ReCoRD v1.0.")
   parser.add_argument("data_file", help="The dataset file in JSON format.")
   parser.add_argument("pred_file", help="The model prediction file in JSON format.")
   parser.add_argument("--output_correct_ids", action="store_true", help="Output the correctly answered query IDs.")
   _lowerCAmelCase       :      Tuple							=							parser.parse_args()
  
   with open(args.data_file) as data_file:
    _lowerCAmelCase       :      int							=							json.load(data_file)
    if dataset_json["version"] != expected_version:
     print(
         F"Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}",
         file=sys.stderr,
     )
    _lowerCAmelCase       :      List[str]							=							dataset_json["data"]
  
   with open(args.pred_file) as pred_file:
    _lowerCAmelCase       :      str							=							json.load(pred_file)
  
   _lowerCAmelCase,		_lowerCAmelCase       :      List[Any]							=							evaluate(dataset, predictions)
  
   if args.output_correct_ids:
    print(F"Output {len(correct_ids)} correctly answered question IDs.")
    with open("correct_ids.json", "w") as f:
     json.dump(correct_ids, f)
  