





"""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       snake_case		(       unittest.TestCase       ):
      lowerCamelCase__      =			(JukeboxModel,) if is_torch_available() else ()
      lowerCamelCase__      =			'''openai/jukebox-1b-lyrics'''
      lowerCamelCase__      =			{
          '''artist''': '''Zac Brown Band''',
          '''genres''': '''Country''',
          '''lyrics''': '''I met a traveller from an antique land,
    Who said "Two vast and trunkless legs of stone
    Stand in the desert. . . . Near them, on the sand,
    Half sunk a shattered visage lies, whose frown,
    And wrinkled lip, and sneer of cold command,
    Tell that its sculptor well those passions read
    Which yet survive, stamped on these lifeless things,
    The hand that mocked them, and the heart that fed;
    And on the pedestal, these words appear:
    My name is Ozymandias, King of Kings;
    Look on my Works, ye Mighty, and despair!
    Nothing beside remains. Round the decay
    Of that colossal Wreck, boundless and bare
    The lone and level sands stretch far away
    ''',
      }
      # fmt: off
      lowerCamelCase__      =			[
          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
      ]

      lowerCamelCase__      =			[
          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
      ]

      lowerCamelCase__      =			[
          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
      ]
      lowerCamelCase__      =			[
          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
      ]

      lowerCamelCase__      =			[
          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
      ]
      lowerCamelCase__      =			[
          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
      ]

      lowerCamelCase__      =			[1058304, 0, 786432, 7169, 507, 76, 27, 40, 30, 76]

      lowerCamelCase__      =			[
          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
      ]
      lowerCamelCase__      =			[
          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
      ]
      lowerCamelCase__      =			[
          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
      ]
      lowerCamelCase__      =			[
          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
      ]
      lowerCamelCase__      =			[
          -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
      ]
      lowerCamelCase__      =			[
          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
      ]
      lowerCamelCase__      =			[
          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
      ]
      lowerCamelCase__      =			[
          76, 27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45, 45, 76, 38, 31, 33,
          45, 76, 41, 32, 76, 45, 46, 41, 40, 31, 78, 76
      ]

      def 						SCREAMING_SNAKE_CASE_      (      self       :int						):
       __SCREAMING_SNAKE_CASE			:      Any 							=	JukeboxTokenizer.from_pretrained(self.model_id						)
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	tokenizer(**self.metas						)['''input_ids''']
       return tokens

      @slow
      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	JukeboxModel.from_pretrained(self.model_id						,							min_duration=0						).eval()
       __SCREAMING_SNAKE_CASE			:      str 							=	self.prepare_inputs()

       set_seed(0						)
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	[torch.zeros(1						,							0						,							dtype=torch.long						).cpu() for _ in range(3						)]
       __SCREAMING_SNAKE_CASE			:      int 							=	model._sample(_lowerCamelCase						,							_lowerCamelCase						,							[0]						,							sample_length=4_0 * model.priors[0].raw_to_tokens						,							save_results=_lowerCamelCase						)
       self.assertIn(zs[0][0].detach().cpu().tolist()						,							[self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]						)

       set_seed(0						)
       __SCREAMING_SNAKE_CASE			:      Any 							=	model._sample(_lowerCamelCase						,							_lowerCamelCase						,							[1]						,							sample_length=4_0 * model.priors[1].raw_to_tokens						,							save_results=_lowerCamelCase						)
       self.assertIn(zs[1][0].detach().cpu().tolist()						,							[self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]						)

       set_seed(0						)
       __SCREAMING_SNAKE_CASE			:      str 							=	model._sample(_lowerCamelCase						,							_lowerCamelCase						,							[2]						,							sample_length=4_0 * model.priors[2].raw_to_tokens						,							save_results=_lowerCamelCase						)
       self.assertIn(zs[2][0].detach().cpu().tolist()						,							[self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]						)

      @slow
      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						):
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	False
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	JukeboxModel.from_pretrained(self.model_id						,							min_duration=0						).eval()

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

       __SCREAMING_SNAKE_CASE			:      str 							=	model.priors[0]
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	0
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	top_prior.get_music_tokens_conds(_lowerCamelCase						,							start=_lowerCamelCase						,							end=start + top_prior.n_ctx						)
       __SCREAMING_SNAKE_CASE			:      str 							=	top_prior.get_metadata(labels[0].clone()						,							_lowerCamelCase						,							1_0_5_8_3_0_4						,							0						)

       self.assertIsNone(_lowerCamelCase						)
       self.assertListEqual(metadata.numpy()[0][:1_0].tolist()						,							self.EXPECTED_Y_COND						)

       __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      str 							=	top_prior.get_cond(_lowerCamelCase						,							_lowerCamelCase						)
       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 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						):
       __SCREAMING_SNAKE_CASE			:      Dict 							=	False

       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	JukeboxModel.from_pretrained(self.model_id						,							min_duration=0						).eval()
       set_seed(0						)
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	torch.rand((1, 5_1_2_0, 1)						)
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	list(self.prepare_inputs()						)

       __SCREAMING_SNAKE_CASE			:      Any 							=	[model.vqvae.encode(_lowerCamelCase						,							start_level=2						,							bs_chunks=waveform.shape[0]						)[0], None, None]
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	model._sample(
           _lowerCamelCase						,							_lowerCamelCase						,							sample_levels=[0]						,							save_results=_lowerCamelCase						,							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						)						)

       __SCREAMING_SNAKE_CASE			:      int 							=	torch.cat((zs[0], torch.zeros(1						,							2_0_4_8 - zs[0].shape[-1]						))						,							dim=-1						).long()
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	[upper_a, model.vqvae.encode(_lowerCamelCase						,							start_level=1						,							bs_chunks=waveform.shape[0]						)[0], None]
       __SCREAMING_SNAKE_CASE			:      str 							=	model._sample(
           _lowerCamelCase						,							_lowerCamelCase						,							sample_levels=[1]						,							save_results=_lowerCamelCase						,							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						)						)

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	torch.cat((zs[1], torch.zeros(1						,							2_0_4_8 - zs[1].shape[-1]						))						,							dim=-1						).long()
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	[upper_a, upper_a, model.vqvae.encode(_lowerCamelCase						,							start_level=0						,							bs_chunks=waveform.shape[0]						)[0]]
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	model._sample(
           _lowerCamelCase						,							_lowerCamelCase						,							sample_levels=[2]						,							save_results=_lowerCamelCase						,							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 						SCREAMING_SNAKE_CASE_      (      self       :Tuple						):
       __SCREAMING_SNAKE_CASE			:      int 							=	JukeboxModel.from_pretrained(self.model_id						,							min_duration=0						).eval()
       set_seed(0						)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	torch.rand((1, 5_1_2_0, 1)						)
       with torch.no_grad():
        __SCREAMING_SNAKE_CASE			:      Tuple 							=	model.vqvae.encode(_lowerCamelCase						,							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():
        __SCREAMING_SNAKE_CASE			:      str 							=	model.vqvae.decode(_lowerCamelCase						,							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       snake_case		(       unittest.TestCase       ):
      lowerCamelCase__      =			(JukeboxModel,) if is_torch_available() else ()
      lowerCamelCase__      =			'''openai/jukebox-5b-lyrics'''
      lowerCamelCase__      =			{
          '''artist''': '''Zac Brown Band''',
          '''genres''': '''Country''',
          '''lyrics''': '''I met a traveller from an antique land,
    Who said "Two vast and trunkless legs of stone
    Stand in the desert. . . . Near them, on the sand,
    Half sunk a shattered visage lies, whose frown,
    And wrinkled lip, and sneer of cold command,
    Tell that its sculptor well those passions read
    Which yet survive, stamped on these lifeless things,
    The hand that mocked them, and the heart that fed;
    And on the pedestal, these words appear:
    My name is Ozymandias, King of Kings;
    Look on my Works, ye Mighty, and despair!
    Nothing beside remains. Round the decay
    Of that colossal Wreck, boundless and bare
    The lone and level sands stretch far away
    ''',
      }

      # fmt: off
      lowerCamelCase__      =			[
          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
      ]
      lowerCamelCase__      =			[
          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
      ]

      lowerCamelCase__      =			[
          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
      ]
      lowerCamelCase__      =			[
          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
      ]

      lowerCamelCase__      =			[
          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
      ]
      lowerCamelCase__      =			[
          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
      ]

      lowerCamelCase__      =			[
          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
      ]
      lowerCamelCase__      =			[
          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
      ]

      lowerCamelCase__      =			[
          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
      ]
      lowerCamelCase__      =			[
          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 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						,							_lowerCamelCase       :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	JukeboxTokenizer.from_pretrained(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	tokenizer(**self.metas						)['''input_ids''']
       return tokens

      @slow
      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						):
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	JukeboxModel.from_pretrained(self.model_id						,							min_duration=0						).eval()
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	self.prepare_inputs(self.model_id						)

       set_seed(0						)
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	[torch.zeros(1						,							0						,							dtype=torch.long						).cpu() for _ in range(3						)]
       __SCREAMING_SNAKE_CASE			:      str 							=	model._sample(_lowerCamelCase						,							_lowerCamelCase						,							[0]						,							sample_length=6_0 * model.priors[0].raw_to_tokens						,							save_results=_lowerCamelCase						)
       self.assertIn(zs[0][0].detach().cpu().tolist()						,							[self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]						)

       set_seed(0						)
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	model._sample(_lowerCamelCase						,							_lowerCamelCase						,							[1]						,							sample_length=6_0 * model.priors[1].raw_to_tokens						,							save_results=_lowerCamelCase						)
       self.assertIn(zs[1][0].detach().cpu().tolist()						,							[self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]						)

       set_seed(0						)
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	model._sample(_lowerCamelCase						,							_lowerCamelCase						,							[2]						,							sample_length=6_0 * model.priors[2].raw_to_tokens						,							save_results=_lowerCamelCase						)
       self.assertIn(zs[2][0].detach().cpu().tolist()						,							[self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]						)

      @slow
      @skip('''Not enough GPU memory on CI runners'''						)
      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE			:      int 							=	JukeboxModel.from_pretrained(self.model_id						,							min_duration=0						).eval()
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	[i.cuda() for i in self.prepare_inputs(self.model_id						)]

       set_seed(0						)
       model.priors[0].cuda()
       __SCREAMING_SNAKE_CASE			:      str 							=	[torch.zeros(1						,							0						,							dtype=torch.long						).cuda() for _ in range(3						)]
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	model._sample(_lowerCamelCase						,							_lowerCamelCase						,							[0]						,							sample_length=6_0 * model.priors[0].raw_to_tokens						,							save_results=_lowerCamelCase						)
       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()
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	model._sample(_lowerCamelCase						,							_lowerCamelCase						,							[1]						,							sample_length=6_0 * model.priors[1].raw_to_tokens						,							save_results=_lowerCamelCase						)
       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()
       __SCREAMING_SNAKE_CASE			:      Any 							=	model._sample(_lowerCamelCase						,							_lowerCamelCase						,							[2]						,							sample_length=6_0 * model.priors[2].raw_to_tokens						,							save_results=_lowerCamelCase						)
       torch.testing.assert_allclose(zs[2][0].cpu()						,							torch.tensor(self.EXPECTED_GPU_OUTPUTS_0						)						)



      @slow
      def 						SCREAMING_SNAKE_CASE_      (      self       :List[Any]						):
       __SCREAMING_SNAKE_CASE			:      Dict 							=	'''ArthurZ/jukebox_prior_0'''
       __SCREAMING_SNAKE_CASE			:      int 							=	JukeboxPrior.from_pretrained(_lowerCamelCase						,							min_duration=0						).eval().half().to('''cuda'''						)

       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	self.prepare_inputs(_lowerCamelCase						)[0].cuda()
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	model.get_metadata(_lowerCamelCase						,							0						,							7_6_8_0						,							0						)
       set_seed(0						)
       __SCREAMING_SNAKE_CASE			:      str 							=	model.sample(1						,							metadata=_lowerCamelCase						,							sample_tokens=6_0						)
       self.assertIn(outputs[0].cpu().tolist()						,							[self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]						)








"""simple docstring"""





import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)




def 		lowerCAmelCase_     (  lowercase_       :							Tuple			,				lowercase_       :							Union[str, Any]=False			,				lowercase_       :							Optional[Any]=False			,				lowercase_       :							Dict="data2vec."		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	'''backbone.''' if is_semantic else ''''''

 __SCREAMING_SNAKE_CASE			:      Any 							=	[]
 for i in range(config.num_hidden_layers		):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (F'''{prefix}blocks.{i}.norm1.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.norm2.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')		)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         (F'''{prefix}cls_token''', F'''{hf_prefix}embeddings.cls_token'''),
         (F'''{prefix}patch_embed.proj.weight''', F'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
         (F'''{prefix}patch_embed.proj.bias''', F'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
     ]		)

 if has_lm_head:
  # mask token + shared relative position bias + layernorm
  rename_keys.extend(
      [
          ('''mask_token''', F'''{hf_prefix}embeddings.mask_token'''),
          (
              '''rel_pos_bias.relative_position_bias_table''',
              F'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
          ),
          (
              '''rel_pos_bias.relative_position_index''',
              F'''{hf_prefix}encoder.relative_position_bias.relative_position_index''',
          ),
          ('''norm.weight''', '''layernorm.weight'''),
          ('''norm.bias''', '''layernorm.bias'''),
      ]		)
 elif is_semantic:
  # semantic segmentation classification heads
  rename_keys.extend(
      [
          ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
          ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
          ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
          ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
      ]		)
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ('''fc_norm.weight''', F'''{hf_prefix}pooler.layernorm.weight'''),
          ('''fc_norm.bias''', F'''{hf_prefix}pooler.layernorm.bias'''),
          ('''head.weight''', '''classifier.weight'''),
          ('''head.bias''', '''classifier.bias'''),
      ]		)

 return rename_keys




def 		lowerCAmelCase_     (  lowercase_       :							Union[str, Any]			,				lowercase_       :							Union[str, Any]			,				lowercase_       :							Optional[Any]=False			,				lowercase_       :							Any=False			,				lowercase_       :							Dict="data2vec_vision."		):

 '''simple docstring'''






 for i in range(config.num_hidden_layers		):
  __SCREAMING_SNAKE_CASE			:      int 							=	'''backbone.''' if is_semantic else ''''''
  # queries, keys and values
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''		)
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''		)
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''		)

  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	in_proj_weight[
      : config.hidden_size, :
  ]
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	q_bias
  __SCREAMING_SNAKE_CASE			:      str 							=	in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __SCREAMING_SNAKE_CASE			:      Dict 							=	in_proj_weight[
      -config.hidden_size :, :
  ]
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''		)
  __SCREAMING_SNAKE_CASE			:      int 							=	state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''		)

  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	gamma_a
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	gamma_a

  # relative_position bias table + index
  if not has_lm_head:
   # each layer has its own relative position bias
   __SCREAMING_SNAKE_CASE			:      Dict 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''		)
   __SCREAMING_SNAKE_CASE			:      int 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''		)

   __SCREAMING_SNAKE_CASE			:      List[str] 							=	table
   __SCREAMING_SNAKE_CASE			:      Any 							=	index




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      List[str] 							=	argparse.ArgumentParser(
     '''Convert Data2VecVision to HF for image classification and pretraining'''			,				add_help=lowercase_		)
 parser.add_argument('''--hf_checkpoint_name'''			,				type=lowercase_		)
 parser.add_argument('''--input_size'''			,				default=224			,				type=lowercase_			,				help='''images input size'''		)
 parser.add_argument('''--beit_checkpoint'''			,				default=''''''			,				help='''beit checkpoint'''		)

 return parser.parse_args()




def 		lowerCAmelCase_     (  lowercase_       :							str			,				lowercase_       :							List[str]			,				lowercase_       :							Union[str, Any]		):

 '''simple docstring'''






 def load_state_dict(lowercase_       :							Optional[Any]			,				lowercase_       :							int			,				lowercase_       :							Union[str, Any]=""			,				lowercase_       :							Optional[int]="relative_position_index"		):
  __SCREAMING_SNAKE_CASE			:      Any 							=	[]
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	[]
  __SCREAMING_SNAKE_CASE			:      Dict 							=	[]
  # copy state_dict so _load_from_state_dict can modify it
  __SCREAMING_SNAKE_CASE			:      List[str] 							=	getattr(lowercase_			,				'''_metadata'''			,				lowercase_		)
  __SCREAMING_SNAKE_CASE			:      str 							=	state_dict.copy()
  if metadata is not None:
   __SCREAMING_SNAKE_CASE			:      int 							=	metadata

  def load(lowercase_       :							Any			,				lowercase_       :							Optional[Any]=""		):
   __SCREAMING_SNAKE_CASE			:      Dict 							=	{} if metadata is None else metadata.get(prefix[:-1]			,				{}		)
   module._load_from_state_dict(
       lowercase_			,				lowercase_			,				lowercase_			,				lowercase_			,				lowercase_			,				lowercase_			,				lowercase_		)
   for name, child in module._modules.items():
    if child is not None:
     load(lowercase_			,				prefix + name + '''.'''		)

  load(lowercase_			,				prefix=lowercase_		)

  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	[]
  __SCREAMING_SNAKE_CASE			:      Dict 							=	[]
  for key in missing_keys:
   __SCREAMING_SNAKE_CASE			:      Dict 							=	True
   for ignore_key in ignore_missing.split('''|'''		):
    if ignore_key in key:
     __SCREAMING_SNAKE_CASE			:      Dict 							=	False
     break
   if keep_flag:
    warn_missing_keys.append(lowercase_		)
   else:
    ignore_missing_keys.append(lowercase_		)

  __SCREAMING_SNAKE_CASE			:      Tuple 							=	warn_missing_keys

  if len(lowercase_		) > 0:
   print(
       '''Weights of {} not initialized from pretrained model: {}'''.format(
           model.__class__.__name__			,				lowercase_		)		)
  if len(lowercase_		) > 0:
   print('''Weights from pretrained model not used in {}: {}'''.format(model.__class__.__name__			,				lowercase_		)		)
  if len(lowercase_		) > 0:
   print(
       '''Ignored weights of {} not initialized from pretrained model: {}'''.format(
           model.__class__.__name__			,				lowercase_		)		)
  if len(lowercase_		) > 0:
   print('''\n'''.join(lowercase_		)		)

 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	{
     '''pretrained''': False,
     '''use_shared_rel_pos_bias''': True,
     '''use_abs_pos_emb''': False,
     '''init_values''': 0.1,
 }

 if is_finetuned:
  model_kwargs.update(
      {
          '''num_classes''': 1000,
          '''use_mean_pooling''': True,
          '''init_scale''': 0.001,
          '''use_rel_pos_bias''': True,
      }		)

 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	create_model(
     '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224'''			,				**lowercase_			,				)
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	model.patch_embed.patch_size
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	(args.input_size // patch_size[0], args.input_size // patch_size[1])
 __SCREAMING_SNAKE_CASE			:      Dict 							=	torch.load(args.beit_checkpoint			,				map_location='''cpu'''		)

 print(F'''Load ckpt from {args.beit_checkpoint}'''		)
 __SCREAMING_SNAKE_CASE			:      Any 							=	None
 for model_key in ("model", "module"):
  if model_key in checkpoint:
   __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	checkpoint[model_key]
   print(F'''Load state_dict by model_key = {model_key}'''		)
   break

 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	list(checkpoint_model.keys()		)
 for key in all_keys:
  if "relative_position_index" in key:
   checkpoint_model.pop(lowercase_		)

  if "relative_position_bias_table" in key:
   __SCREAMING_SNAKE_CASE			:      str 							=	checkpoint_model[key]
   __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Optional[int] 							=	rel_pos_bias.size()
   __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	model.state_dict()[key].size()
   __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	model.patch_embed.patch_shape
   if dst_patch_shape[0] != dst_patch_shape[1]:
    raise NotImplementedError()

 load_state_dict(lowercase_			,				lowercase_			,				prefix=''''''		)

 return model




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	get_args()

 __SCREAMING_SNAKE_CASE			:      int 							=	'''ft1k''' in args.hf_checkpoint_name
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	'''large''' in args.hf_checkpoint_name

 if is_finetuned:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
  # into this folder.
  import modeling_finetune  # noqa: F401
 else:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
  # into this folder
  # IMPORTANT: Note that for now we've only converted the down-stream
  # model and not the full pretrained model. This means for the integration
  # test you need to add a `return x` after the following line:
  # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
  # to make the integration test pass.
  import modeling_cyclical  # noqa: F401

 # 1. Create model config
 __SCREAMING_SNAKE_CASE			:      Dict 							=	DataaVecVisionConfig()
 if is_finetuned:
  __SCREAMING_SNAKE_CASE			:      int 							=	True
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	False
  __SCREAMING_SNAKE_CASE			:      Dict 							=	True
  __SCREAMING_SNAKE_CASE			:      int 							=	1000

  __SCREAMING_SNAKE_CASE			:      Any 							=	'''huggingface/label-files'''
  __SCREAMING_SNAKE_CASE			:      Dict 							=	'''imagenet-1k-id2label.json'''
  __SCREAMING_SNAKE_CASE			:      Dict 							=	json.load(open(hf_hub_download(lowercase_			,				lowercase_			,				repo_type='''dataset'''		)			,				'''r'''		)		)
  __SCREAMING_SNAKE_CASE			:      Dict 							=	{int(lowercase_		): v for k, v in idalabel.items()}
  __SCREAMING_SNAKE_CASE			:      List[str] 							=	idalabel
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	{v: k for k, v in idalabel.items()}
 else:
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	False
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	True
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	False

 if is_large:
  __SCREAMING_SNAKE_CASE			:      str 							=	1024
  __SCREAMING_SNAKE_CASE			:      Any 							=	4096
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	24
  __SCREAMING_SNAKE_CASE			:      Tuple 							=	16

 # 2. Load Beit model
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	load_beit_model(lowercase_			,				lowercase_			,				lowercase_		)
 orig_model.eval()

 # 3. Forward Beit model
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	BeitImageProcessor(size=config.image_size			,				do_center_crop=lowercase_		)
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	Image.open('''../../../../tests/fixtures/tests_samples/COCO/000000039769.png'''		)
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	image_processor(images=lowercase_			,				return_tensors='''pt'''		)
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	encoding['''pixel_values''']

 __SCREAMING_SNAKE_CASE			:      Tuple 							=	(pixel_values,) if is_finetuned else (pixel_values, None)
 with torch.no_grad():
  __SCREAMING_SNAKE_CASE			:      Tuple 							=	orig_model(*lowercase_		)

 # 4. Load HF Data2VecVision model
 if is_finetuned:
  __SCREAMING_SNAKE_CASE			:      Dict 							=	DataaVecVisionForImageClassification(lowercase_		)
  hf_model.eval()
  __SCREAMING_SNAKE_CASE			:      Any 							=	False
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	'''data2vec_vision.'''
 else:
  __SCREAMING_SNAKE_CASE			:      Dict 							=	DataaVecVisionModel(lowercase_		)
  hf_model.eval()
  __SCREAMING_SNAKE_CASE			:      str 							=	True
  __SCREAMING_SNAKE_CASE			:      Any 							=	''''''

 __SCREAMING_SNAKE_CASE			:      Dict 							=	create_rename_keys(lowercase_			,				hf_prefix=lowercase_			,				has_lm_head=lowercase_		)
 __SCREAMING_SNAKE_CASE			:      Dict 							=	orig_model.state_dict()
 for src, dest in rename_keys:
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	state_dict.pop(lowercase_		)
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	val

 read_in_q_k_v(lowercase_			,				lowercase_			,				hf_prefix=lowercase_			,				has_lm_head=lowercase_		)
 __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      List[Any] 							=	hf_model.load_state_dict(lowercase_			,				strict=lowercase_		)
 print('''HF missing'''			,				lowercase_		)
 print('''HF unexpected_keys'''			,				lowercase_		)

 # 5. Forward HF Data2VecVision model
 with torch.no_grad():
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	hf_model(lowercase_		)

 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

 # 6. Compare
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	torch.max(torch.abs(hf_output - orig_model_output		)		).item()

 print(F'''max_absolute_diff = {max_absolute_diff}'''		)
 __SCREAMING_SNAKE_CASE			:      str 							=	torch.allclose(lowercase_			,				lowercase_			,				atol=1E-3		)
 print('''Do both models output the same tensors?'''			,				'''🔥''' if success else '''💩'''		)
 if not success:
  raise Exception('''Something went wRoNg'''		)

 # 7. Save
 print(F'''Saving to {args.hf_checkpoint_name}'''		)
 hf_model.save_pretrained(args.hf_checkpoint_name		)
 image_processor.save_pretrained(args.hf_checkpoint_name		)


if __name__ == "__main__":
    main()
    # Run the following to convert checkpoints
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./pretrained_base.pt \
    #          --hf_checkpoint_name "./data2vec-vision-base"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./finetuned_base.pt \
    #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./pretrained_large.pt \
    #          --hf_checkpoint_name "./data2vec-vision-large"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./finetuned_large.pt \
    #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"

