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 			lowerCamelCase_			(unittest.TestCase					):
      '''simple docstring'''

      __UpperCamelCase:						Union[str, Any]      			=		(JukeboxModel,) if is_torch_available() else ()
      __UpperCamelCase:						Optional[int]      			=		"openai/jukebox-1b-lyrics"
      __UpperCamelCase:						Optional[Any]      			=		{
          "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
      __UpperCamelCase:						Any      			=		[
          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
      ]

      __UpperCamelCase:						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
      ]

      __UpperCamelCase:						Union[str, Any]      			=		[
          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
      ]
      __UpperCamelCase:						int      			=		[
          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
      ]

      __UpperCamelCase:						str      			=		[
          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
      ]
      __UpperCamelCase:						Optional[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
      ]

      __UpperCamelCase:						List[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]

      __UpperCamelCase:						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
      ]
      __UpperCamelCase:						Any      			=		[
          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
      ]
      __UpperCamelCase:						Optional[int]      			=		[
          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
      ]
      __UpperCamelCase:						Union[str, Any]      			=		[
          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
      ]
      __UpperCamelCase:						Tuple      			=		[
          -0.0_492, -0.0_524, -0.0_565, -0.0_640, -0.0_686, -0.0_684, -0.0_677, -0.0_664,
          -0.0_605, -0.0_490, -0.0_330, -0.0_168, -0.0_083, -0.0_075, -0.0_051, 0.0_025,
          0.0_136, 0.0_261, 0.0_386, 0.0_497, 0.0_580, 0.0_599, 0.0_583, 0.0_614,
          0.0_740, 0.0_889, 0.1_023, 0.1_162, 0.1_211, 0.1_212, 0.1_251, 0.1_336,
          0.1_502, 0.1_686, 0.1_883, 0.2_148, 0.2_363, 0.2_458, 0.2_507, 0.2_531
      ]
      __UpperCamelCase:						Dict      			=		[
          0.0_256, -0.0_544, 0.1_600, -0.0_032, 0.1_066, 0.0_825, -0.0_013, 0.3_440,
          0.0_210, 0.0_412, -0.1_777, -0.0_892, -0.0_164, 0.0_285, -0.0_613, -0.0_617,
          -0.0_137, -0.0_201, -0.0_175, 0.0_215, -0.0_627, 0.0_520, -0.0_730, 0.0_970,
          -0.0_100, 0.0_442, -0.0_586, 0.0_207, -0.0_015, -0.0_082
      ]
      __UpperCamelCase:						Dict      			=		[
          0.0_415, 0.0_877, 0.0_022, -0.0_055, 0.0_751, 0.0_334, 0.0_324, -0.0_068,
          0.0_011, 0.0_017, -0.0_676, 0.0_655, -0.0_143, 0.0_399, 0.0_303, 0.0_743,
          -0.0_168, -0.0_394, -0.1_113, 0.0_124, 0.0_442, 0.0_267, -0.0_003, -0.1_536,
          -0.0_116, -0.1_837, -0.0_180, -0.1_026, -0.0_777, -0.0_456
      ]
      __UpperCamelCase:						Optional[Any]      			=		[
          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   _A			( self		:       Tuple ):
             _UpperCAmelCase  :						Dict							   =					JukeboxTokenizer.from_pretrained(self.model_id )
             _UpperCAmelCase  :						int							   =					tokenizer(**self.metas )["input_ids"]
             return tokens




      @slow
      def   _A			( self		:       List[Any] ):
             _UpperCAmelCase  :						Tuple							   =					JukeboxModel.from_pretrained(self.model_id		,      min_duration=0 ).eval()
             _UpperCAmelCase  :						Any							   =					self.prepare_inputs()

             set_seed(0 )
             _UpperCAmelCase  :						Optional[int]							   =					[torch.zeros(1		,      0		,      dtype=torch.long ).cpu() for _ in range(3 )]
             _UpperCAmelCase  :						List[str]							   =					model._sample(A		,      A		,      [0]		,      sample_length=40 * 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  :						Any							   =					model._sample(A		,      A		,      [1]		,      sample_length=40 * 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=40 * 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   _A			( self		:       Optional[int] ):
             _UpperCAmelCase  :						str							   =					False
             _UpperCAmelCase  :						Dict							   =					JukeboxModel.from_pretrained(self.model_id		,      min_duration=0 ).eval()

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

             _UpperCAmelCase  :						Tuple							   =					model.priors[0]
             _UpperCAmelCase  :						Dict							   =					0
             _UpperCAmelCase  :						Optional[int]							   =					top_prior.get_music_tokens_conds(A		,      start=A		,      end=start + top_prior.n_ctx )
             _UpperCAmelCase  :						str							   =					top_prior.get_metadata(labels[0].clone()		,      A		,      1058304		,      0 )

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

             _UpperCAmelCase  :						Tuple							   =					top_prior.get_cond(A		,      A )
             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   _A			( self		:       Optional[Any] ):
             _UpperCAmelCase  :						int							   =					False

             _UpperCAmelCase  :						Dict							   =					JukeboxModel.from_pretrained(self.model_id		,      min_duration=0 ).eval()
             set_seed(0 )
             _UpperCAmelCase  :						Any							   =					torch.rand((1, 5120, 1) )
             _UpperCAmelCase  :						Tuple							   =					list(self.prepare_inputs() )

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

             _UpperCAmelCase  :						Optional[Any]							   =					torch.cat((zs[0], torch.zeros(1		,      2048 - zs[0].shape[-1] ))		,      dim=-1 ).long()
             _UpperCAmelCase  :						Any							   =					[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=40 * model.priors[1].raw_to_tokens )
             torch.testing.assert_allclose(zs[1][0][:40]		,      torch.tensor(self.EXPECTED_PRIMED_1 ) )

             _UpperCAmelCase  :						Union[str, Any]							   =					torch.cat((zs[1], torch.zeros(1		,      2048 - zs[1].shape[-1] ))		,      dim=-1 ).long()
             _UpperCAmelCase  :						Optional[Any]							   =					[upper_a, upper_a, model.vqvae.encode(A		,      start_level=0		,      bs_chunks=waveform.shape[0] )[0]]
             _UpperCAmelCase  :						List[str]							   =					model._sample(
                 A		,      A		,      sample_levels=[2]		,      save_results=A		,      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   _A			( self		:       Optional[Any] ):
             _UpperCAmelCase  :						List[Any]							   =					JukeboxModel.from_pretrained(self.model_id		,      min_duration=0 ).eval()
             set_seed(0 )
             _UpperCAmelCase  :						Union[str, Any]							   =					torch.rand((1, 5120, 1) )
             with torch.no_grad():
                    _UpperCAmelCase  :						str							   =					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  :						List[str]							   =					model.vqvae.decode(A		,      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 			lowerCamelCase_			(unittest.TestCase					):
      '''simple docstring'''

      __UpperCamelCase:						Any      			=		(JukeboxModel,) if is_torch_available() else ()
      __UpperCamelCase:						Any      			=		"openai/jukebox-5b-lyrics"
      __UpperCamelCase:						Optional[int]      			=		{
          "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
      __UpperCamelCase:						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,
          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
      ]
      __UpperCamelCase:						Dict      			=		[
          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
      ]

      __UpperCamelCase:						List[str]      			=		[
          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
      ]
      __UpperCamelCase:						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, 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
      ]

      __UpperCamelCase:						Optional[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
      ]
      __UpperCamelCase:						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
      ]

      __UpperCamelCase:						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,
          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
      ]
      __UpperCamelCase:						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, 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
      ]

      __UpperCamelCase:						Optional[Any]      			=		[
          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
      ]
      __UpperCamelCase:						List[Any]      			=		[
          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   _A			( self		:       Union[str, Any]		,      A		:       List[Any] ):
             _UpperCAmelCase  :						Dict							   =					JukeboxTokenizer.from_pretrained(A )
             _UpperCAmelCase  :						Optional[Any]							   =					tokenizer(**self.metas )["input_ids"]
             return tokens




      @slow
      def   _A			( self		:       List[Any] ):
             _UpperCAmelCase  :						List[Any]							   =					JukeboxModel.from_pretrained(self.model_id		,      min_duration=0 ).eval()
             _UpperCAmelCase  :						List[str]							   =					self.prepare_inputs(self.model_id )

             set_seed(0 )
             _UpperCAmelCase  :						Optional[int]							   =					[torch.zeros(1		,      0		,      dtype=torch.long ).cpu() for _ in range(3 )]
             _UpperCAmelCase  :						Union[str, Any]							   =					model._sample(A		,      A		,      [0]		,      sample_length=60 * 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  :						Tuple							   =					model._sample(A		,      A		,      [1]		,      sample_length=60 * 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  :						List[Any]							   =					model._sample(A		,      A		,      [2]		,      sample_length=60 * 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   _A			( self		:       Tuple ):
             _UpperCAmelCase  :						Optional[Any]							   =					JukeboxModel.from_pretrained(self.model_id		,      min_duration=0 ).eval()
             _UpperCAmelCase  :						Optional[Any]							   =					[i.cuda() for i in self.prepare_inputs(self.model_id )]

             set_seed(0 )
             model.priors[0].cuda()
             _UpperCAmelCase  :						Union[str, Any]							   =					[torch.zeros(1		,      0		,      dtype=torch.long ).cuda() for _ in range(3 )]
             _UpperCAmelCase  :						Tuple							   =					model._sample(A		,      A		,      [0]		,      sample_length=60 * 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  :						List[str]							   =					model._sample(A		,      A		,      [1]		,      sample_length=60 * 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  :						Any							   =					model._sample(A		,      A		,      [2]		,      sample_length=60 * 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   _A			( self		:       Optional[Any] ):
             _UpperCAmelCase  :						Union[str, Any]							   =					"ArthurZ/jukebox_prior_0"
             _UpperCAmelCase  :						Optional[Any]							   =					JukeboxPrior.from_pretrained(A		,      min_duration=0 ).eval().half().to("cuda" )

             _UpperCAmelCase  :						int							   =					self.prepare_inputs(A )[0].cuda()
             _UpperCAmelCase  :						Any							   =					model.get_metadata(A		,      0		,      7680		,      0 )
             set_seed(0 )
             _UpperCAmelCase  :						Dict							   =					model.sample(1		,      metadata=A		,      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_torch_available,
)


__SCREAMING_SNAKE_CASE :  Union[str, Any]  =						{
    """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
    """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE :  Optional[int]  =						[
      """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTSanJapaneseForConditionalGeneration""",
      """GPTSanJapaneseModel""",
      """GPTSanJapanesePreTrainedModel""",
  ]
  __SCREAMING_SNAKE_CASE :  Union[str, Any]  =						[
      """GPTSanJapaneseTokenizer""",
  ]


if TYPE_CHECKING:
  from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
  from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gptsan_japanese import (
        GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTSanJapaneseForConditionalGeneration,
        GPTSanJapaneseModel,
        GPTSanJapanesePreTrainedModel,
    )
    from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
  import sys

  __SCREAMING_SNAKE_CASE :  Union[str, Any]  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

