




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"""

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

    snake_case__					=      [
        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
    ]

    snake_case__					=      [
        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
    ]
    snake_case__					=      [
        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
    ]

    snake_case__					=      [
        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
    ]
    snake_case__					=      [
        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
    ]

    snake_case__					=      [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]

    snake_case__					=      [
        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
    ]
    snake_case__					=      [
        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
    ]
    snake_case__					=      [
        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
    ]
    snake_case__					=      [
        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
    ]
    snake_case__					=      [
        -0.0_4_9_2, -0.0_5_2_4, -0.0_5_6_5, -0.0_6_4_0, -0.0_6_8_6, -0.0_6_8_4, -0.0_6_7_7, -0.0_6_6_4,
        -0.0_6_0_5, -0.0_4_9_0, -0.0_3_3_0, -0.0_1_6_8, -0.0_0_8_3, -0.0_0_7_5, -0.0_0_5_1, 0.0_0_2_5,
        0.0_1_3_6, 0.0_2_6_1, 0.0_3_8_6, 0.0_4_9_7, 0.0_5_8_0, 0.0_5_9_9, 0.0_5_8_3, 0.0_6_1_4,
        0.0_7_4_0, 0.0_8_8_9, 0.1_0_2_3, 0.1_1_6_2, 0.1_2_1_1, 0.1_2_1_2, 0.1_2_5_1, 0.1_3_3_6,
        0.1_5_0_2, 0.1_6_8_6, 0.1_8_8_3, 0.2_1_4_8, 0.2_3_6_3, 0.2_4_5_8, 0.2_5_0_7, 0.2_5_3_1
    ]
    snake_case__					=      [
        0.0_2_5_6, -0.0_5_4_4, 0.1_6_0_0, -0.0_0_3_2, 0.1_0_6_6, 0.0_8_2_5, -0.0_0_1_3, 0.3_4_4_0,
        0.0_2_1_0, 0.0_4_1_2, -0.1_7_7_7, -0.0_8_9_2, -0.0_1_6_4, 0.0_2_8_5, -0.0_6_1_3, -0.0_6_1_7,
        -0.0_1_3_7, -0.0_2_0_1, -0.0_1_7_5, 0.0_2_1_5, -0.0_6_2_7, 0.0_5_2_0, -0.0_7_3_0, 0.0_9_7_0,
        -0.0_1_0_0, 0.0_4_4_2, -0.0_5_8_6, 0.0_2_0_7, -0.0_0_1_5, -0.0_0_8_2
    ]
    snake_case__					=      [
        0.0_4_1_5, 0.0_8_7_7, 0.0_0_2_2, -0.0_0_5_5, 0.0_7_5_1, 0.0_3_3_4, 0.0_3_2_4, -0.0_0_6_8,
        0.0_0_1_1, 0.0_0_1_7, -0.0_6_7_6, 0.0_6_5_5, -0.0_1_4_3, 0.0_3_9_9, 0.0_3_0_3, 0.0_7_4_3,
        -0.0_1_6_8, -0.0_3_9_4, -0.1_1_1_3, 0.0_1_2_4, 0.0_4_4_2, 0.0_2_6_7, -0.0_0_0_3, -0.1_5_3_6,
        -0.0_1_1_6, -0.1_8_3_7, -0.0_1_8_0, -0.1_0_2_6, -0.0_7_7_7, -0.0_4_5_6
    ]
    snake_case__					=      [
        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     :     int   )      ->       Optional[int]:
         lowerCAmelCase__		= JukeboxTokenizer.from_pretrained(self.model_id   )
         lowerCAmelCase__		= tokenizer(**self.metas   )["input_ids"]
         return tokens


    @slow
    def    a      (       self     :     Tuple   )      ->       int:
         lowerCAmelCase__		= JukeboxModel.from_pretrained(self.model_id   ,	min_duration=0   ).eval()
         lowerCAmelCase__		= self.prepare_inputs()

         set_seed(0   )
         lowerCAmelCase__		= [torch.zeros(1   ,	0   ,	dtype=torch.long   ).cpu() for _ in range(3   )]
         lowerCAmelCase__		= 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   )
         lowerCAmelCase__		= 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   )
         lowerCAmelCase__		= 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    a      (       self     :     str   )      ->       Dict:
         lowerCAmelCase__		= False
         lowerCAmelCase__		= JukeboxModel.from_pretrained(self.model_id   ,	min_duration=0   ).eval()

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

         lowerCAmelCase__		= model.priors[0]
         lowerCAmelCase__		= 0
         lowerCAmelCase__		= top_prior.get_music_tokens_conds(SCREAMING_SNAKE_CASE__   ,	start=SCREAMING_SNAKE_CASE__   ,	end=start + top_prior.n_ctx   )
         lowerCAmelCase__		= top_prior.get_metadata(labels[0].clone()   ,	SCREAMING_SNAKE_CASE__   ,	1_058_304   ,	0   )

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

         lowerCAmelCase__	,		lowerCAmelCase__	,		lowerCAmelCase__		= 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    a      (       self     :     Optional[int]   )      ->       Optional[Any]:
         lowerCAmelCase__		= False

         lowerCAmelCase__		= JukeboxModel.from_pretrained(self.model_id   ,	min_duration=0   ).eval()
         set_seed(0   )
         lowerCAmelCase__		= torch.rand((1, 5_120, 1)   )
         lowerCAmelCase__		= list(self.prepare_inputs()   )

         lowerCAmelCase__		= [model.vqvae.encode(SCREAMING_SNAKE_CASE__   ,	start_level=2   ,	bs_chunks=waveform.shape[0]   )[0], None, None]
         lowerCAmelCase__		= 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   )   )

         lowerCAmelCase__		= torch.cat((zs[0], torch.zeros(1   ,	2_048 - zs[0].shape[-1]   ))   ,	dim=-1   ).long()
         lowerCAmelCase__		= [upper_a, model.vqvae.encode(SCREAMING_SNAKE_CASE__   ,	start_level=1   ,	bs_chunks=waveform.shape[0]   )[0], None]
         lowerCAmelCase__		= 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   )   )

         lowerCAmelCase__		= torch.cat((zs[1], torch.zeros(1   ,	2_048 - zs[1].shape[-1]   ))   ,	dim=-1   ).long()
         lowerCAmelCase__		= [upper_a, upper_a, model.vqvae.encode(SCREAMING_SNAKE_CASE__   ,	start_level=0   ,	bs_chunks=waveform.shape[0]   )[0]]
         lowerCAmelCase__		= 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    a      (       self     :     Tuple   )      ->       List[Any]:
         lowerCAmelCase__		= JukeboxModel.from_pretrained(self.model_id   ,	min_duration=0   ).eval()
         set_seed(0   )
         lowerCAmelCase__		= torch.rand((1, 5_120, 1)   )
         with torch.no_grad():
              lowerCAmelCase__		= 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():
              lowerCAmelCase__		= 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  __lowerCamelCase       (    unittest.TestCase    ):




    """simple docstring"""

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

    snake_case__					=      [
        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
    ]
    snake_case__					=      [
        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
    ]

    snake_case__					=      [
        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
    ]
    snake_case__					=      [
        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
    ]

    snake_case__					=      [
        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
    ]
    snake_case__					=      [
        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
    ]

    snake_case__					=      [
        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
    ]
    snake_case__					=      [
        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     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     Tuple   )      ->       int:
         lowerCAmelCase__		= JukeboxTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= tokenizer(**self.metas   )["input_ids"]
         return tokens


    @slow
    def    a      (       self     :     str   )      ->       str:
         lowerCAmelCase__		= JukeboxModel.from_pretrained(self.model_id   ,	min_duration=0   ).eval()
         lowerCAmelCase__		= self.prepare_inputs(self.model_id   )

         set_seed(0   )
         lowerCAmelCase__		= [torch.zeros(1   ,	0   ,	dtype=torch.long   ).cpu() for _ in range(3   )]
         lowerCAmelCase__		= 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   )
         lowerCAmelCase__		= 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   )
         lowerCAmelCase__		= 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    a      (       self     :     Any   )      ->       str:
         lowerCAmelCase__		= JukeboxModel.from_pretrained(self.model_id   ,	min_duration=0   ).eval()
         lowerCAmelCase__		= [i.cuda() for i in self.prepare_inputs(self.model_id   )]

         set_seed(0   )
         model.priors[0].cuda()
         lowerCAmelCase__		= [torch.zeros(1   ,	0   ,	dtype=torch.long   ).cuda() for _ in range(3   )]
         lowerCAmelCase__		= 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()
         lowerCAmelCase__		= 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()
         lowerCAmelCase__		= 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    a      (       self     :     Optional[Any]   )      ->       Union[str, Any]:
         lowerCAmelCase__		= "ArthurZ/jukebox_prior_0"
         lowerCAmelCase__		= JukeboxPrior.from_pretrained(SCREAMING_SNAKE_CASE__   ,	min_duration=0   ).eval().half().to("cuda"   )

         lowerCAmelCase__		= self.prepare_inputs(SCREAMING_SNAKE_CASE__   )[0].cuda()
         lowerCAmelCase__		= model.get_metadata(SCREAMING_SNAKE_CASE__   ,	0   ,	7_680   ,	0   )
         set_seed(0   )
         lowerCAmelCase__		= 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]   )






import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


UpperCamelCase								=     logging.get_logger(__name__)

if is_vision_available():
     import PIL.Image
     from PIL import Image
     from PIL.Image import Image as ImageType
else:
     UpperCamelCase								=     object

if is_torch_available():
     import torch

if is_soundfile_availble():
     import soundfile as sf





class  __lowerCamelCase       :




    """simple docstring"""

    def __init__(       self     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     List[str]   )      ->       Union[str, Any]:
         lowerCAmelCase__		= value


    def __str__(       self     :     int   )      ->       List[Any]:
         return self.to_string()


    def    a      (       self     :     str   )      ->       Union[str, Any]:
         logger.error(
             "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"   )
         return self._value


    def    a      (       self     :     str   )      ->       str:
         logger.error(
             "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"   )
         return str(self._value   )






class  __lowerCamelCase       (    UpperCamelCase__	,				UpperCamelCase__    ):




    """simple docstring"""

    def    a      (       self     :     Optional[Any]   )      ->       int:
         return self._value


    def    a      (       self     :     Optional[Any]   )      ->       Any:
         return self._value






class  __lowerCamelCase       (    UpperCamelCase__	,				UpperCamelCase__    ):




    """simple docstring"""

    def __init__(       self     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]   )      ->       Optional[int]:
         super().__init__(SCREAMING_SNAKE_CASE__   )

         if not is_vision_available():
              raise ImportError("PIL must be installed in order to handle images."   )

         lowerCAmelCase__		= None
         lowerCAmelCase__		= None
         lowerCAmelCase__		= None

         if isinstance(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ):
              lowerCAmelCase__		= value
         elif isinstance(SCREAMING_SNAKE_CASE__   ,	(str, pathlib.Path)   ):
              lowerCAmelCase__		= value
         elif isinstance(SCREAMING_SNAKE_CASE__   ,	torch.Tensor   ):
              lowerCAmelCase__		= value
         else:
              raise ValueError(f'Unsupported type for {self.__class__.__name__}: {type(SCREAMING_SNAKE_CASE__   )}'   )


    def    a      (       self     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     Any=None   ,	SCREAMING_SNAKE_CASE__     :     str=None   )      ->       Union[str, Any]:
         from IPython.display import Image, display

         display(Image(self.to_string()   )   )


    def    a      (       self     :     List[Any]   )      ->       int:
         if self._raw is not None:
              return self._raw

         if self._path is not None:
              lowerCAmelCase__		= Image.open(self._path   )
              return self._raw


    def    a      (       self     :     Optional[int]   )      ->       Union[str, Any]:
         if self._path is not None:
              return self._path

         if self._raw is not None:
              lowerCAmelCase__		= tempfile.mkdtemp()
              lowerCAmelCase__		= os.path.join(SCREAMING_SNAKE_CASE__   ,	str(uuid.uuida()   ) + ".png"   )
              self._raw.save(self._path   )

              return self._path

         if self._tensor is not None:
              lowerCAmelCase__		= self._tensor.cpu().detach().numpy()

              # There is likely simpler than load into image into save
              lowerCAmelCase__		= Image.fromarray((array * 255).astype(np.uinta   )   )

              lowerCAmelCase__		= tempfile.mkdtemp()
              lowerCAmelCase__		= os.path.join(SCREAMING_SNAKE_CASE__   ,	str(uuid.uuida()   ) + ".png"   )

              img.save(self._path   )

              return self._path






class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

    def __init__(       self     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     int=16_000   )      ->       Union[str, Any]:
         super().__init__(SCREAMING_SNAKE_CASE__   )

         if not is_soundfile_availble():
              raise ImportError("soundfile must be installed in order to handle audio."   )

         lowerCAmelCase__		= None
         lowerCAmelCase__		= None

         lowerCAmelCase__		= samplerate

         if isinstance(SCREAMING_SNAKE_CASE__   ,	(str, pathlib.Path)   ):
              lowerCAmelCase__		= value
         elif isinstance(SCREAMING_SNAKE_CASE__   ,	torch.Tensor   ):
              lowerCAmelCase__		= value
         else:
              raise ValueError(f'Unsupported audio type: {type(SCREAMING_SNAKE_CASE__   )}'   )


    def    a      (       self     :     str   ,	SCREAMING_SNAKE_CASE__     :     List[str]=None   ,	SCREAMING_SNAKE_CASE__     :     List[str]=None   )      ->       Any:
         from IPython.display import Audio, display

         display(Audio(self.to_string()   ,	rate=self.samplerate   )   )


    def    a      (       self     :     Optional[Any]   )      ->       List[Any]:
         if self._tensor is not None:
              return self._tensor

         if self._path is not None:
              lowerCAmelCase__	,		lowerCAmelCase__		= sf.read(self._path   )
              lowerCAmelCase__		= torch.tensor(SCREAMING_SNAKE_CASE__   )
              return self._tensor


    def    a      (       self     :     Dict   )      ->       List[str]:
         if self._path is not None:
              return self._path

         if self._tensor is not None:
              lowerCAmelCase__		= tempfile.mkdtemp()
              lowerCAmelCase__		= os.path.join(SCREAMING_SNAKE_CASE__   ,	str(uuid.uuida()   ) + ".wav"   )
              sf.write(self._path   ,	self._tensor   ,	samplerate=self.samplerate   )
              return self._path






UpperCamelCase								=     {'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
UpperCamelCase								=     {str: AgentText}

if is_vision_available():
     UpperCamelCase								=     AgentImage



def 							_A   (						*lowerCAmelCase_     :					List[str]   ,				**lowerCAmelCase_     :					Optional[int]			):
     """simple docstring"""

     lowerCAmelCase__		= [(arg.to_raw() if isinstance(lowerCAmelCase_   ,				lowerCAmelCase_			) else arg) for arg in args]
     lowerCAmelCase__		= {k: (v.to_raw() if isinstance(lowerCAmelCase_   ,				lowerCAmelCase_			) else v) for k, v in kwargs.items()}
     return args, kwargs






def 							_A   (						lowerCAmelCase_     :					Dict   ,				lowerCAmelCase_     :					Optional[int]=None			):
     """simple docstring"""

     if isinstance(lowerCAmelCase_   ,				lowerCAmelCase_			):
          lowerCAmelCase__		= {}
          for i, (k, v) in enumerate(outputs.items()			):
               if output_types is not None:
                    # If the class has defined outputs, we can map directly according to the class definition
                    if output_types[i] in AGENT_TYPE_MAPPING:
                         lowerCAmelCase__		= AGENT_TYPE_MAPPING[output_types[i]](lowerCAmelCase_			)
                    else:
                         lowerCAmelCase__		= AgentType(lowerCAmelCase_			)

               else:
                    # If the class does not have defined output, then we map according to the type
                    for _k, _v in INSTANCE_TYPE_MAPPING.items():
                         if isinstance(lowerCAmelCase_   ,				_k			):
                              lowerCAmelCase__		= _v(lowerCAmelCase_			)
                    if k not in decoded_outputs:
                         lowerCAmelCase__		= AgentType[v]

     elif isinstance(lowerCAmelCase_   ,				(list, tuple)			):
          lowerCAmelCase__		= type(lowerCAmelCase_			)()
          for i, v in enumerate(lowerCAmelCase_			):
               if output_types is not None:
                    # If the class has defined outputs, we can map directly according to the class definition
                    if output_types[i] in AGENT_TYPE_MAPPING:
                         decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](lowerCAmelCase_			)			)
                    else:
                         decoded_outputs.append(AgentType(lowerCAmelCase_			)			)
               else:
                    # If the class does not have defined output, then we map according to the type
                    lowerCAmelCase__		= False
                    for _k, _v in INSTANCE_TYPE_MAPPING.items():
                         if isinstance(lowerCAmelCase_   ,				_k			):
                              decoded_outputs.append(_v(lowerCAmelCase_			)			)
                              lowerCAmelCase__		= True

                    if not found:
                         decoded_outputs.append(AgentType(lowerCAmelCase_			)			)

     else:
          if output_types[0] in AGENT_TYPE_MAPPING:
               # If the class has defined outputs, we can map directly according to the class definition
               lowerCAmelCase__		= AGENT_TYPE_MAPPING[output_types[0]](lowerCAmelCase_			)

          else:
               # If the class does not have defined output, then we map according to the type
               for _k, _v in INSTANCE_TYPE_MAPPING.items():
                    if isinstance(lowerCAmelCase_   ,				_k			):
                         return _v(lowerCAmelCase_			)
               return AgentType(lowerCAmelCase_			)

     return decoded_outputs
