


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ConvNextImageProcessor


class 	a__						(    unittest.TestCase     ):

       def __init__( self   :  str   ,				lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  Optional[Any]=7   ,				lowerCamelCase_   :  Optional[int]=3   ,				lowerCamelCase_   :  List[str]=1_8   ,				lowerCamelCase_   :  Tuple=3_0   ,				lowerCamelCase_   :  Tuple=4_0_0   ,				lowerCamelCase_   :  Dict=True   ,				lowerCamelCase_   :  Union[str, Any]=None   ,				lowerCamelCase_   :  Optional[int]=0.8_7_5   ,				lowerCamelCase_   :  Optional[int]=True   ,				lowerCamelCase_   :  Dict=[0.5, 0.5, 0.5]   ,				lowerCamelCase_   :  List[str]=[0.5, 0.5, 0.5]   ,				):
            a_     :  List[Any]				      =     size if size is not None else {"""shortest_edge""": 2_0}
            a_     :  List[str]				      =     parent
            a_     :  Union[str, Any]				      =     batch_size
            a_     :  List[str]				      =     num_channels
            a_     :  List[str]				      =     image_size
            a_     :  Optional[Any]				      =     min_resolution
            a_     :  Any				      =     max_resolution
            a_     :  List[str]				      =     do_resize
            a_     :  Dict				      =     size
            a_     :  Optional[Any]				      =     crop_pct
            a_     :  List[Any]				      =     do_normalize
            a_     :  Dict				      =     image_mean
            a_     :  List[Any]				      =     image_std

       def     UpperCAmelCase( self   :  Dict     ):
            return {
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_normalize": self.do_normalize,
                "do_resize": self.do_resize,
                "size": self.size,
                "crop_pct": self.crop_pct,
            }


@require_torch
@require_vision
class 	a__						(    lowerCAmelCase_    ,							unittest.TestCase     ):
       lowerCamelCase__:  Tuple				     =  ConvNextImageProcessor if is_vision_available() else None

       def     UpperCAmelCase( self   :  List[Any]     ):
            a_     :  Tuple				      =     ConvNextImageProcessingTester(self     )

       @property
       def     UpperCAmelCase( self   :  Any     ):
            return self.image_processor_tester.prepare_image_processor_dict()

       def     UpperCAmelCase( self   :  str     ):
            a_     :  Union[str, Any]				      =     self.image_processing_class(**self.image_processor_dict     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""do_resize"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""size"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""crop_pct"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""do_normalize"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""image_mean"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""image_std"""     )     )

       def     UpperCAmelCase( self   :  Optional[int]     ):
            a_     :  Tuple				      =     self.image_processing_class.from_dict(self.image_processor_dict     )
            self.assertEqual(image_processor.size   ,				{"""shortest_edge""": 2_0}     )

            a_     :  str				      =     self.image_processing_class.from_dict(self.image_processor_dict   ,				size=4_2     )
            self.assertEqual(image_processor.size   ,				{"""shortest_edge""": 4_2}     )

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            pass

       def     UpperCAmelCase( self   :  Any     ):
            # Initialize image_processing
            a_     :  Union[str, Any]				      =     self.image_processing_class(**self.image_processor_dict     )
            # create random PIL images
            a_     :  Any				      =     prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=lowerCamelCase_     )
            for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase_   ,				Image.Image     )

            # Test not batched input
            a_     :  Union[str, Any]				      =     image_processing(image_inputs[0]   ,				return_tensors="""pt"""     ).pixel_values
            self.assertEqual(
                encoded_images.shape   ,				(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )   ,				)

            # Test batched
            a_     :  Optional[Any]				      =     image_processing(lowerCamelCase_   ,				return_tensors="""pt"""     ).pixel_values
            self.assertEqual(
                encoded_images.shape   ,				(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )   ,				)

       def     UpperCAmelCase( self   :  Tuple     ):
            # Initialize image_processing
            a_     :  Optional[int]				      =     self.image_processing_class(**self.image_processor_dict     )
            # create random numpy tensors
            a_     :  int				      =     prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=lowerCamelCase_   ,				numpify=lowerCamelCase_     )
            for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase_   ,				np.ndarray     )

            # Test not batched input
            a_     :  List[str]				      =     image_processing(image_inputs[0]   ,				return_tensors="""pt"""     ).pixel_values
            self.assertEqual(
                encoded_images.shape   ,				(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )   ,				)

            # Test batched
            a_     :  Tuple				      =     image_processing(lowerCamelCase_   ,				return_tensors="""pt"""     ).pixel_values
            self.assertEqual(
                encoded_images.shape   ,				(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )   ,				)

       def     UpperCAmelCase( self   :  str     ):
            # Initialize image_processing
            a_     :  str				      =     self.image_processing_class(**self.image_processor_dict     )
            # create random PyTorch tensors
            a_     :  Optional[int]				      =     prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=lowerCamelCase_   ,				torchify=lowerCamelCase_     )
            for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase_   ,				torch.Tensor     )

            # Test not batched input
            a_     :  Dict				      =     image_processing(image_inputs[0]   ,				return_tensors="""pt"""     ).pixel_values
            self.assertEqual(
                encoded_images.shape   ,				(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )   ,				)

            # Test batched
            a_     :  str				      =     image_processing(lowerCamelCase_   ,				return_tensors="""pt"""     ).pixel_values
            self.assertEqual(
                encoded_images.shape   ,				(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )   ,				)









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):
      UpperCAmelCase__ :      Optional[Any]       						=  (JukeboxModel,) if is_torch_available() else ()
      UpperCAmelCase__ :      Union[str, Any]       						=  'openai/jukebox-1b-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__ :      Dict       						=  [
          1864, 1536, 1213, 1870, 1357, 1536, 519, 880, 1323, 789, 1082, 534,
          1000, 1445, 1105, 1130, 967, 515, 1434, 1620, 534, 1495, 283, 1445,
          333, 1307, 539, 1631, 1528, 375, 1434, 673, 627, 710, 778, 1883,
          1405, 1276, 1455, 1228
      ]

      UpperCAmelCase__ :      List[Any]       						=  [
          1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653
      ]

      UpperCAmelCase__ :      str       						=  [
          1125, 1751, 697, 1776, 1141, 1476, 391, 697, 1125, 684, 867, 416,
          844, 1372, 1274, 717, 1274, 844, 1299, 1419, 697, 1370, 317, 1125,
          191, 1440, 1370, 1440, 1370, 282, 1621, 1370, 368, 349, 867, 1872,
          1262, 869, 1728, 747
      ]
      UpperCAmelCase__ :      str       						=  [
          416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416
      ]

      UpperCAmelCase__ :      str       						=  [
          1755, 842, 307, 1843, 1022, 1395, 234, 1554, 806, 739, 1022, 442,
          616, 556, 268, 1499, 933, 457, 1440, 1837, 755, 985, 308, 902,
          293, 1443, 1671, 1141, 1533, 555, 1562, 1061, 287, 417, 1022, 2008,
          1186, 1015, 1777, 268
      ]
      UpperCAmelCase__ :      Tuple       						=  [
          854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842,
          185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842,
          591, 842, 1353, 842, 185, 842, 591, 842, 591, 114, 591, 842,
          185, 842, 591, 89
      ]

      UpperCAmelCase__ :      List[Any]       						=  [105_8304, 0, 78_6432, 7169, 507, 76, 27, 40, 30, 76]

      UpperCAmelCase__ :      Union[str, Any]       						=  [
          390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002,
          1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002,
          1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907,
          1907, 1788, 596, 1626
      ]
      UpperCAmelCase__ :      List[str]       						=  [
          1236, 1668, 1484, 1920, 1848, 1409, 139, 864, 1828, 1272, 1599, 824,
          1672, 139, 555, 1484, 824, 1920, 555, 596, 1579, 1599, 1231, 1599,
          1637, 1407, 212, 824, 1599, 116, 1433, 824, 258, 1599, 1433, 1895,
          1063, 1433, 1433, 1599
      ]
      UpperCAmelCase__ :      str       						=  [
          1684, 1873, 1119, 1189, 395, 611, 1901, 972, 890, 1337, 1392, 1927,
          96, 972, 672, 780, 1119, 890, 158, 771, 1073, 1927, 353, 1331,
          1269, 1459, 1333, 1645, 812, 1577, 1337, 606, 353, 981, 1466, 619,
          197, 391, 302, 1930
      ]
      UpperCAmelCase__ :      Union[str, Any]       						=  [
          390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002,
          1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002,
          1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907,
          1907, 1788, 596, 1626
      ]
      UpperCAmelCase__ :      Optional[int]       						=  [
          -0.04_92, -0.05_24, -0.05_65, -0.06_40, -0.06_86, -0.06_84, -0.06_77, -0.06_64,
          -0.06_05, -0.04_90, -0.03_30, -0.01_68, -0.00_83, -0.00_75, -0.00_51, 0.00_25,
          0.01_36, 0.02_61, 0.03_86, 0.04_97, 0.05_80, 0.05_99, 0.05_83, 0.06_14,
          0.07_40, 0.08_89, 0.10_23, 0.11_62, 0.12_11, 0.12_12, 0.12_51, 0.13_36,
          0.15_02, 0.16_86, 0.18_83, 0.21_48, 0.23_63, 0.24_58, 0.25_07, 0.25_31
      ]
      UpperCAmelCase__ :      Union[str, Any]       						=  [
          0.02_56, -0.05_44, 0.16_00, -0.00_32, 0.10_66, 0.08_25, -0.00_13, 0.34_40,
          0.02_10, 0.04_12, -0.17_77, -0.08_92, -0.01_64, 0.02_85, -0.06_13, -0.06_17,
          -0.01_37, -0.02_01, -0.01_75, 0.02_15, -0.06_27, 0.05_20, -0.07_30, 0.09_70,
          -0.01_00, 0.04_42, -0.05_86, 0.02_07, -0.00_15, -0.00_82
      ]
      UpperCAmelCase__ :      str       						=  [
          0.04_15, 0.08_77, 0.00_22, -0.00_55, 0.07_51, 0.03_34, 0.03_24, -0.00_68,
          0.00_11, 0.00_17, -0.06_76, 0.06_55, -0.01_43, 0.03_99, 0.03_03, 0.07_43,
          -0.01_68, -0.03_94, -0.11_13, 0.01_24, 0.04_42, 0.02_67, -0.00_03, -0.15_36,
          -0.01_16, -0.18_37, -0.01_80, -0.10_26, -0.07_77, -0.04_56
      ]
      UpperCAmelCase__ :      int       						=  [
          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      lowerCAmelCase__		(							self:					Dict  ):
             __lowerCamelCase  =		JukeboxTokenizer.from_pretrained(self.model_id  )
             __lowerCamelCase  =		tokenizer(**self.metas  )["""input_ids"""]
             return tokens
      @slow
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             __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(UpperCamelCase_       ,			UpperCamelCase_       ,			[0]       ,			sample_length=40 * model.priors[0].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             self.assertIn(zs[0][0].detach().cpu().tolist()       ,			[self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]  )

             set_seed(0  )
             __lowerCamelCase  =		model._sample(UpperCamelCase_       ,			UpperCamelCase_       ,			[1]       ,			sample_length=40 * model.priors[1].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             self.assertIn(zs[1][0].detach().cpu().tolist()       ,			[self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]  )

             set_seed(0  )
             __lowerCamelCase  =		model._sample(UpperCamelCase_       ,			UpperCamelCase_       ,			[2]       ,			sample_length=40 * model.priors[2].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             self.assertIn(zs[2][0].detach().cpu().tolist()       ,			[self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]  )
      @slow
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             __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(UpperCamelCase_       ,			start=UpperCamelCase_       ,			end=start + top_prior.n_ctx  )
             __lowerCamelCase  =		top_prior.get_metadata(labels[0].clone()       ,			UpperCamelCase_       ,			1_05_83_04       ,			0  )

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

             __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase  =		top_prior.get_cond(UpperCamelCase_       ,			UpperCamelCase_  )
             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      lowerCAmelCase__		(							self:					List[Any]  ):
             __lowerCamelCase  =		False

             __lowerCamelCase  =		JukeboxModel.from_pretrained(self.model_id       ,			min_duration=0  ).eval()
             set_seed(0  )
             __lowerCamelCase  =		torch.rand((1, 51_20, 1)  )
             __lowerCamelCase  =		list(self.prepare_inputs()  )

             __lowerCamelCase  =		[model.vqvae.encode(UpperCamelCase_       ,			start_level=2       ,			bs_chunks=waveform.shape[0]  )[0], None, None]
             __lowerCamelCase  =		model._sample(
                 UpperCamelCase_       ,			UpperCamelCase_       ,			sample_levels=[0]       ,			save_results=UpperCamelCase_       ,			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       ,			20_48 - zs[0].shape[-1]  ))       ,			dim=-1  ).long()
             __lowerCamelCase  =		[upper_a, model.vqvae.encode(UpperCamelCase_       ,			start_level=1       ,			bs_chunks=waveform.shape[0]  )[0], None]
             __lowerCamelCase  =		model._sample(
                 UpperCamelCase_       ,			UpperCamelCase_       ,			sample_levels=[1]       ,			save_results=UpperCamelCase_       ,			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       ,			20_48 - zs[1].shape[-1]  ))       ,			dim=-1  ).long()
             __lowerCamelCase  =		[upper_a, upper_a, model.vqvae.encode(UpperCamelCase_       ,			start_level=0       ,			bs_chunks=waveform.shape[0]  )[0]]
             __lowerCamelCase  =		model._sample(
                 UpperCamelCase_       ,			UpperCamelCase_       ,			sample_levels=[2]       ,			save_results=UpperCamelCase_       ,			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      lowerCAmelCase__		(							self:					List[Any]  ):
             __lowerCamelCase  =		JukeboxModel.from_pretrained(self.model_id       ,			min_duration=0  ).eval()
             set_seed(0  )
             __lowerCamelCase  =		torch.rand((1, 51_20, 1)  )
             with torch.no_grad():
                    __lowerCamelCase  =		model.vqvae.encode(UpperCamelCase_       ,			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(UpperCamelCase_       ,			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):
      UpperCAmelCase__ :      List[Any]       						=  (JukeboxModel,) if is_torch_available() else ()
      UpperCAmelCase__ :      Optional[int]       						=  'openai/jukebox-5b-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__ :      Optional[int]       						=  [
          1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          1489, 1489, 1489, 1489, 1150, 1853, 1509, 1150, 1357, 1509, 6, 1272
      ]
      UpperCAmelCase__ :      Union[str, Any]       						=  [
          1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
      ]

      UpperCAmelCase__ :      Optional[int]       						=  [
          1125, 416, 1125, 1125, 1125, 1125, 1125, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
      ]
      UpperCAmelCase__ :      List[str]       						=  [
          416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416
      ]

      UpperCAmelCase__ :      Optional[Any]       						=  [
          1755, 1061, 234, 1755, 1061, 1755, 185, 290, 307, 307, 616, 616,
          616, 616, 616, 616, 307, 290, 417, 1755, 234, 1755, 185, 290,
          290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1755,
          234, 234, 1755, 234, 185, 185, 307, 616, 616, 616, 616, 290,
          1755, 1755, 1755, 234, 234, 1755, 1572, 290, 307, 616, 34, 616
      ]
      UpperCAmelCase__ :      str       						=  [
          854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842, 185,
          417, 185, 842, 307, 842, 591, 842, 185, 842, 185, 842, 591, 842,
          1353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591,
          89, 591, 842, 591, 842, 591, 417, 1372, 842, 1372, 842, 34, 842,
          185, 89, 591, 842, 185, 842, 591, 632
      ]

      UpperCAmelCase__ :      List[Any]       						=  [
          1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
      ]
      UpperCAmelCase__ :      Dict       						=  [
          1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 1853, 1177, 1536, 1228,
          710, 475, 1489, 1229, 1224, 231, 1224, 252, 1434, 653, 475,
          1106, 1877, 1599, 1228, 1600, 1683, 1182, 1853, 475, 1864,
          252, 1229, 1434, 2001
      ]

      UpperCAmelCase__ :      Optional[int]       						=  [
          1125, 1125, 416, 1125, 1125, 416, 1125, 1125, 416, 416, 1125, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
      ]
      UpperCAmelCase__ :      str       						=  [
          491, 1755, 34, 1613, 1755, 417, 992, 1613, 222, 842, 1353, 1613,
          844, 632, 185, 1613, 844, 632, 185, 1613, 185, 842, 677, 1613,
          185, 114, 1353, 1613, 307, 89, 844, 1613, 307, 1332, 234, 1979,
          307, 89, 1353, 616, 34, 842, 185, 842, 34, 842, 185, 842,
          307, 114, 185, 89, 34, 1268, 185, 89, 34, 842, 185, 89
      ]
      def      lowerCAmelCase__		(							self:					Optional[Any]       ,			UpperCamelCase_:					Optional[int]  ):
             __lowerCamelCase  =		JukeboxTokenizer.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		tokenizer(**self.metas  )["""input_ids"""]
             return tokens
      @slow
      def      lowerCAmelCase__		(							self:					Tuple  ):
             __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(UpperCamelCase_       ,			UpperCamelCase_       ,			[0]       ,			sample_length=60 * model.priors[0].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             self.assertIn(zs[0][0].detach().cpu().tolist()       ,			[self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]  )

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

             set_seed(0  )
             __lowerCamelCase  =		model._sample(UpperCamelCase_       ,			UpperCamelCase_       ,			[2]       ,			sample_length=60 * model.priors[2].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             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      lowerCAmelCase__		(							self:					List[Any]  ):
             __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(UpperCamelCase_       ,			UpperCamelCase_       ,			[0]       ,			sample_length=60 * model.priors[0].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             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(UpperCamelCase_       ,			UpperCamelCase_       ,			[1]       ,			sample_length=60 * model.priors[1].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             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(UpperCamelCase_       ,			UpperCamelCase_       ,			[2]       ,			sample_length=60 * model.priors[2].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             torch.testing.assert_allclose(zs[2][0].cpu()       ,			torch.tensor(self.EXPECTED_GPU_OUTPUTS_0  )  )




      @slow
      def      lowerCAmelCase__		(							self:					Dict  ):
             __lowerCamelCase  =		"""ArthurZ/jukebox_prior_0"""
             __lowerCamelCase  =		JukeboxPrior.from_pretrained(UpperCamelCase_       ,			min_duration=0  ).eval().half().to("""cuda"""  )

             __lowerCamelCase  =		self.prepare_inputs(UpperCamelCase_  )[0].cuda()
             __lowerCamelCase  =		model.get_metadata(UpperCamelCase_       ,			0       ,			76_80       ,			0  )
             set_seed(0  )
             __lowerCamelCase  =		model.sample(1       ,			metadata=UpperCamelCase_       ,			sample_tokens=60  )
             self.assertIn(outputs[0].cpu().tolist()       ,			[self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]  )

