




'''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 			A    (		unittest.TestCase    ):
							lowercase_							=				(JukeboxModel,) if is_torch_available() else ()
							lowercase_							=				'openai/jukebox-1b-lyrics'
							lowercase_							=				{
							    '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
							lowercase_							=				[
							    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
							]

							lowercase_							=				[
							    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
							]

							lowercase_							=				[
							    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
							]
							lowercase_							=				[
							    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
							]

							lowercase_							=				[
							    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
							]
							lowercase_							=				[
							    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
							]

							lowercase_							=				[105_8304, 0, 78_6432, 7169, 507, 76, 27, 40, 30, 76]

							lowercase_							=				[
							    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
							]
							lowercase_							=				[
							    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
							]
							lowercase_							=				[
							    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
							]
							lowercase_							=				[
							    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
							]
							lowercase_							=				[
							    -0.0492, -0.0524, -0.0565, -0.0640, -0.0686, -0.0684, -0.0677, -0.0664,
							    -0.0605, -0.0490, -0.0330, -0.0168, -0.0083, -0.0075, -0.0051, 0.0025,
							    0.0136, 0.0261, 0.0386, 0.0497, 0.0580, 0.0599, 0.0583, 0.0614,
							    0.0740, 0.0889, 0.1023, 0.1162, 0.1211, 0.1212, 0.1251, 0.1336,
							    0.1502, 0.1686, 0.1883, 0.2148, 0.2363, 0.2458, 0.2507, 0.2531
							]
							lowercase_							=				[
							    0.0256, -0.0544, 0.1600, -0.0032, 0.1066, 0.0825, -0.0013, 0.3440,
							    0.0210, 0.0412, -0.1777, -0.0892, -0.0164, 0.0285, -0.0613, -0.0617,
							    -0.0137, -0.0201, -0.0175, 0.0215, -0.0627, 0.0520, -0.0730, 0.0970,
							    -0.0100, 0.0442, -0.0586, 0.0207, -0.0015, -0.0082
							]
							lowercase_							=				[
							    0.0415, 0.0877, 0.0022, -0.0055, 0.0751, 0.0334, 0.0324, -0.0068,
							    0.0011, 0.0017, -0.0676, 0.0655, -0.0143, 0.0399, 0.0303, 0.0743,
							    -0.0168, -0.0394, -0.1113, 0.0124, 0.0442, 0.0267, -0.0003, -0.1536,
							    -0.0116, -0.1837, -0.0180, -0.1026, -0.0777, -0.0456
							]
							lowercase_							=				[
							    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       :  List[Any]					)			->							Optional[Any]:


														"""simple docstring"""


														_a	     =				JukeboxTokenizer.from_pretrained(self.model_id					)
														_a	     =				tokenizer(**self.metas					)['''input_ids''']
														return tokens







							@slow
							def    __lowerCAmelCase			(  self       :  Tuple					)			->							Optional[int]:


														"""simple docstring"""


														_a	     =				JukeboxModel.from_pretrained(self.model_id							,			min_duration=0					).eval()
														_a	     =				self.prepare_inputs()

														set_seed(0					)
														_a	     =				[torch.zeros(1							,			0							,			dtype=torch.long					).cpu() for _ in range(3					)]
														_a	     =				model._sample(lowerCAmelCase_							,			lowerCAmelCase_							,			[0]							,			sample_length=40 * 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					)
														_a	     =				model._sample(lowerCAmelCase_							,			lowerCAmelCase_							,			[1]							,			sample_length=40 * 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					)
														_a	     =				model._sample(lowerCAmelCase_							,			lowerCAmelCase_							,			[2]							,			sample_length=40 * 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    __lowerCAmelCase			(  self       :  Dict					)			->							Union[str, Any]:


														"""simple docstring"""


														_a	     =				False
														_a	     =				JukeboxModel.from_pretrained(self.model_id							,			min_duration=0					).eval()

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

														_a	     =				model.priors[0]
														_a	     =				0
														_a	     =				top_prior.get_music_tokens_conds(lowerCAmelCase_							,			start=lowerCAmelCase_							,			end=start + top_prior.n_ctx					)
														_a	     =				top_prior.get_metadata(labels[0].clone()							,			lowerCAmelCase_							,			1_05_83_04							,			0					)

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

														_a     ,							_a     ,							_a	     =				top_prior.get_cond(lowerCAmelCase_							,			lowerCAmelCase_					)
														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       :  int					)			->							Union[str, Any]:


														"""simple docstring"""


														_a	     =				False

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

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

														_a	     =				torch.cat((zs[0], torch.zeros(1							,			20_48 - zs[0].shape[-1]					))							,			dim=-1					).long()
														_a	     =				[upper_a, model.vqvae.encode(lowerCAmelCase_							,			start_level=1							,			bs_chunks=waveform.shape[0]					)[0], None]
														_a	     =				model._sample(
														    lowerCAmelCase_							,			lowerCAmelCase_							,			sample_levels=[1]							,			save_results=lowerCAmelCase_							,			sample_length=40 * model.priors[1].raw_to_tokens					)
														torch.testing.assert_allclose(zs[1][0][:40]							,			torch.tensor(self.EXPECTED_PRIMED_1					)					)

														_a	     =				torch.cat((zs[1], torch.zeros(1							,			20_48 - zs[1].shape[-1]					))							,			dim=-1					).long()
														_a	     =				[upper_a, upper_a, model.vqvae.encode(lowerCAmelCase_							,			start_level=0							,			bs_chunks=waveform.shape[0]					)[0]]
														_a	     =				model._sample(
														    lowerCAmelCase_							,			lowerCAmelCase_							,			sample_levels=[2]							,			save_results=lowerCAmelCase_							,			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]					)			->							Any:


														"""simple docstring"""


														_a	     =				JukeboxModel.from_pretrained(self.model_id							,			min_duration=0					).eval()
														set_seed(0					)
														_a	     =				torch.rand((1, 51_20, 1)					)
														with torch.no_grad():
																					_a	     =				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():
																					_a	     =				model.vqvae.decode(lowerCAmelCase_							,			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 			A    (		unittest.TestCase    ):
							lowercase_							=				(JukeboxModel,) if is_torch_available() else ()
							lowercase_							=				'openai/jukebox-5b-lyrics'
							lowercase_							=				{
							    '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
							lowercase_							=				[
							    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
							]
							lowercase_							=				[
							    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
							]

							lowercase_							=				[
							    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
							]
							lowercase_							=				[
							    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
							]

							lowercase_							=				[
							    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
							]
							lowercase_							=				[
							    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
							]

							lowercase_							=				[
							    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
							]
							lowercase_							=				[
							    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
							]

							lowercase_							=				[
							    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
							]
							lowercase_							=				[
							    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[int]							,			lowerCAmelCase_       :  Any					)			->							Dict:


														"""simple docstring"""


														_a	     =				JukeboxTokenizer.from_pretrained(lowerCAmelCase_					)
														_a	     =				tokenizer(**self.metas					)['''input_ids''']
														return tokens







							@slow
							def    __lowerCAmelCase			(  self       :  Union[str, Any]					)			->							Union[str, Any]:


														"""simple docstring"""


														_a	     =				JukeboxModel.from_pretrained(self.model_id							,			min_duration=0					).eval()
														_a	     =				self.prepare_inputs(self.model_id					)

														set_seed(0					)
														_a	     =				[torch.zeros(1							,			0							,			dtype=torch.long					).cpu() for _ in range(3					)]
														_a	     =				model._sample(lowerCAmelCase_							,			lowerCAmelCase_							,			[0]							,			sample_length=60 * 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					)
														_a	     =				model._sample(lowerCAmelCase_							,			lowerCAmelCase_							,			[1]							,			sample_length=60 * 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					)
														_a	     =				model._sample(lowerCAmelCase_							,			lowerCAmelCase_							,			[2]							,			sample_length=60 * 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    __lowerCAmelCase			(  self       :  Optional[int]					)			->							Dict:


														"""simple docstring"""


														_a	     =				JukeboxModel.from_pretrained(self.model_id							,			min_duration=0					).eval()
														_a	     =				[i.cuda() for i in self.prepare_inputs(self.model_id					)]

														set_seed(0					)
														model.priors[0].cuda()
														_a	     =				[torch.zeros(1							,			0							,			dtype=torch.long					).cuda() for _ in range(3					)]
														_a	     =				model._sample(lowerCAmelCase_							,			lowerCAmelCase_							,			[0]							,			sample_length=60 * 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()
														_a	     =				model._sample(lowerCAmelCase_							,			lowerCAmelCase_							,			[1]							,			sample_length=60 * 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()
														_a	     =				model._sample(lowerCAmelCase_							,			lowerCAmelCase_							,			[2]							,			sample_length=60 * 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    __lowerCAmelCase			(  self       :  List[str]					)			->							int:


														"""simple docstring"""


														_a	     =				'''ArthurZ/jukebox_prior_0'''
														_a	     =				JukeboxPrior.from_pretrained(lowerCAmelCase_							,			min_duration=0					).eval().half().to('''cuda'''					)

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







'''simple docstring'''


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_reformer import ReformerTokenizer
else:
				_snake_case      :      int   							=		None


_snake_case      :      Union[str, Any]   							=		logging.get_logger(__name__)


_snake_case      :      Union[str, Any]   							=		'▁'

_snake_case      :      Any   							=		{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case      :      List[str]   							=		{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

_snake_case      :      Union[str, Any]   							=		{
    'google/reformer-crime-and-punishment': 524288,
}

class 			A    (		_a    ):
							lowercase_							=				VOCAB_FILES_NAMES
							lowercase_							=				PRETRAINED_VOCAB_FILES_MAP
							lowercase_							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_							=				['input_ids', 'attention_mask']
							lowercase_							=				ReformerTokenizer







							def __init__(  self       :  Any							,			lowerCAmelCase_       :  int=None							,			lowerCAmelCase_       :  Optional[Any]=None							,			lowerCAmelCase_       :  List[str]="</s>"							,			lowerCAmelCase_       :  Any="<unk>"							,			lowerCAmelCase_       :  List[str]=[]							,			**lowerCAmelCase_       :  List[Any]							,			)			->							Dict:


														"""simple docstring"""


														super().__init__(
														    lowerCAmelCase_							,			tokenizer_file=lowerCAmelCase_							,			eos_token=lowerCAmelCase_							,			unk_token=lowerCAmelCase_							,			additional_special_tokens=lowerCAmelCase_							,			**lowerCAmelCase_							,			)

														_a	     =				vocab_file
														_a	     =				False if not self.vocab_file else True







							def    __lowerCAmelCase			(  self       :  List[Any]							,			lowerCAmelCase_       :  str							,			lowerCAmelCase_       :  Optional[str] = None					)			->							Tuple[str]:


														"""simple docstring"""


														if not self.can_save_slow_tokenizer:
																					raise ValueError(
																					    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																					    '''tokenizer.'''					)

														if not os.path.isdir(lowerCAmelCase_					):
																					logger.error(F'Vocabulary path ({save_directory}) should be a directory'					)
																					return
														_a	     =				os.path.join(
														    lowerCAmelCase_							,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

														if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase_					):
																					copyfile(self.vocab_file							,			lowerCAmelCase_					)

														return (out_vocab_file,)

