



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 		lowercase						( unittest.TestCase):






	"""simple docstring"""


	a__      :					str		   =							(JukeboxModel,) if is_torch_available() else ()
	a__      :					Tuple		   =							"openai/jukebox-1b-lyrics"
	a__      :					List[str]		   =							{
	    "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
	a__      :					str		   =							[
	    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
	]

	a__      :					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
	]

	a__      :					Tuple		   =							[
	    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
	]
	a__      :					int		   =							[
	    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
	]

	a__      :					List[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
	]
	a__      :					Optional[Any]		   =							[
	    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
	]

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

	a__      :					str		   =							[
	    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
	]
	a__      :					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
	]
	a__      :					Union[str, Any]		   =							[
	    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
	]
	a__      :					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
	]
	a__      :					Tuple		   =							[
	    -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
	]
	a__      :					int		   =							[
	    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
	]
	a__      :					int		   =							[
	    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
	]
	a__      :					Tuple		   =							[
	    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	:						str     )			-> Optional[Any]:
							UpperCAmelCase_=   JukeboxTokenizer.from_pretrained(self.model_id     )
							UpperCAmelCase_=   tokenizer(**self.metas     )["""input_ids"""]
							return tokens




	@slow
	def   _SCREAMING_SNAKE_CASE		(	self	:						Union[str, Any]     )			-> List[Any]:
							UpperCAmelCase_=   JukeboxModel.from_pretrained(self.model_id						,  min_duration=0     ).eval()
							UpperCAmelCase_=   self.prepare_inputs()

							set_seed(0     )
							UpperCAmelCase_=   [torch.zeros(1						,  0						,  dtype=torch.long     ).cpu() for _ in range(3     )]
							UpperCAmelCase_=   model._sample(_snake_case						,  _snake_case						,  [0]						,  sample_length=40 * model.priors[0].raw_to_tokens						,  save_results=_snake_case     )
							self.assertIn(zs[0][0].detach().cpu().tolist()						,  [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]     )

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

							set_seed(0     )
							UpperCAmelCase_=   model._sample(_snake_case						,  _snake_case						,  [2]						,  sample_length=40 * model.priors[2].raw_to_tokens						,  save_results=_snake_case     )
							self.assertIn(zs[2][0].detach().cpu().tolist()						,  [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]     )




	@slow
	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]     )			-> List[Any]:
							UpperCAmelCase_=   False
							UpperCAmelCase_=   JukeboxModel.from_pretrained(self.model_id						,  min_duration=0     ).eval()

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

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

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

							UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_=   top_prior.get_cond(_snake_case						,  _snake_case     )
							torch.testing.assert_allclose(
							    audio_conditioning[0][0][:30].detach()						,  torch.tensor(self.EXPECTED_AUDIO_COND     )						,  atol=1E-4						,  rtol=1E-4     )
							torch.testing.assert_allclose(
							    metadata_conditioning[0][0][:30].detach()						,  torch.tensor(self.EXPECTED_META_COND     )						,  atol=1E-4						,  rtol=1E-4     )
							torch.testing.assert_allclose(
							    lyric_tokens[0, :30].detach()						,  torch.tensor(self.EXPECTED_LYRIC_COND     )						,  atol=1E-4						,  rtol=1E-4     )




	@slow
	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]     )			-> Any:
							UpperCAmelCase_=   False

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

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

							UpperCAmelCase_=   torch.cat((zs[0], torch.zeros(1						,  2_048 - zs[0].shape[-1]     ))						,  dim=-1     ).long()
							UpperCAmelCase_=   [upper_a, model.vqvae.encode(_snake_case						,  start_level=1						,  bs_chunks=waveform.shape[0]     )[0], None]
							UpperCAmelCase_=   model._sample(
							    _snake_case						,  _snake_case						,  sample_levels=[1]						,  save_results=_snake_case						,  sample_length=40 * model.priors[1].raw_to_tokens     )
							torch.testing.assert_allclose(zs[1][0][:40]						,  torch.tensor(self.EXPECTED_PRIMED_1     )     )

							UpperCAmelCase_=   torch.cat((zs[1], torch.zeros(1						,  2_048 - zs[1].shape[-1]     ))						,  dim=-1     ).long()
							UpperCAmelCase_=   [upper_a, upper_a, model.vqvae.encode(_snake_case						,  start_level=0						,  bs_chunks=waveform.shape[0]     )[0]]
							UpperCAmelCase_=   model._sample(
							    _snake_case						,  _snake_case						,  sample_levels=[2]						,  save_results=_snake_case						,  sample_length=40 * model.priors[2].raw_to_tokens     )
							torch.testing.assert_allclose(zs[2][0][:40].cpu()						,  torch.tensor(self.EXPECTED_PRIMED_2     )     )




	@slow
	def   _SCREAMING_SNAKE_CASE		(	self	:						List[str]     )			-> Any:
							UpperCAmelCase_=   JukeboxModel.from_pretrained(self.model_id						,  min_duration=0     ).eval()
							set_seed(0     )
							UpperCAmelCase_=   torch.rand((1, 5_120, 1)     )
							with torch.no_grad():
													UpperCAmelCase_=   model.vqvae.encode(_snake_case						,  start_level=2						,  bs_chunks=x.shape[0]     )
							torch.testing.assert_allclose(zs[0][0]						,  torch.tensor(self.EXPECTED_VQVAE_ENCODE     )     )

							with torch.no_grad():
													UpperCAmelCase_=   model.vqvae.decode(_snake_case						,  start_level=2						,  bs_chunks=x.shape[0]     )
							torch.testing.assert_allclose(x[0, :40, 0]						,  torch.tensor(self.EXPECTED_VQVAE_DECODE     )						,  atol=1E-4						,  rtol=1E-4     )






@require_torch
class 		lowercase						( unittest.TestCase):






	"""simple docstring"""


	a__      :					str		   =							(JukeboxModel,) if is_torch_available() else ()
	a__      :					Tuple		   =							"openai/jukebox-5b-lyrics"
	a__      :					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
	a__      :					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,
	    1489, 1489, 1489, 1489, 1150, 1853, 1509, 1150, 1357, 1509, 6, 1272
	]
	a__      :					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
	]

	a__      :					str		   =							[
	    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
	]
	a__      :					Optional[int]		   =							[
	    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
	]

	a__      :					List[str]		   =							[
	    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
	]
	a__      :					List[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
	]

	a__      :					List[str]		   =							[
	    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
	]
	a__      :					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
	]

	a__      :					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
	]
	a__      :					List[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   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]						,  __UpperCAmelCase	:						int     )			-> Dict:
							UpperCAmelCase_=   JukeboxTokenizer.from_pretrained(_snake_case     )
							UpperCAmelCase_=   tokenizer(**self.metas     )["""input_ids"""]
							return tokens




	@slow
	def   _SCREAMING_SNAKE_CASE		(	self	:						Any     )			-> Any:
							UpperCAmelCase_=   JukeboxModel.from_pretrained(self.model_id						,  min_duration=0     ).eval()
							UpperCAmelCase_=   self.prepare_inputs(self.model_id     )

							set_seed(0     )
							UpperCAmelCase_=   [torch.zeros(1						,  0						,  dtype=torch.long     ).cpu() for _ in range(3     )]
							UpperCAmelCase_=   model._sample(_snake_case						,  _snake_case						,  [0]						,  sample_length=60 * model.priors[0].raw_to_tokens						,  save_results=_snake_case     )
							self.assertIn(zs[0][0].detach().cpu().tolist()						,  [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]     )

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

							set_seed(0     )
							UpperCAmelCase_=   model._sample(_snake_case						,  _snake_case						,  [2]						,  sample_length=60 * model.priors[2].raw_to_tokens						,  save_results=_snake_case     )
							self.assertIn(zs[2][0].detach().cpu().tolist()						,  [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]     )




	@slow
	@skip("""Not enough GPU memory on CI runners"""     )
	def   _SCREAMING_SNAKE_CASE		(	self	:						List[str]     )			-> List[Any]:
							UpperCAmelCase_=   JukeboxModel.from_pretrained(self.model_id						,  min_duration=0     ).eval()
							UpperCAmelCase_=   [i.cuda() for i in self.prepare_inputs(self.model_id     )]

							set_seed(0     )
							model.priors[0].cuda()
							UpperCAmelCase_=   [torch.zeros(1						,  0						,  dtype=torch.long     ).cuda() for _ in range(3     )]
							UpperCAmelCase_=   model._sample(_snake_case						,  _snake_case						,  [0]						,  sample_length=60 * model.priors[0].raw_to_tokens						,  save_results=_snake_case     )
							torch.testing.assert_allclose(zs[0][0].cpu()						,  torch.tensor(self.EXPECTED_GPU_OUTPUTS_2     )     )
							model.priors[0].cpu()

							set_seed(0     )
							model.priors[1].cuda()
							UpperCAmelCase_=   model._sample(_snake_case						,  _snake_case						,  [1]						,  sample_length=60 * model.priors[1].raw_to_tokens						,  save_results=_snake_case     )
							torch.testing.assert_allclose(zs[1][0].cpu()						,  torch.tensor(self.EXPECTED_GPU_OUTPUTS_1     )     )
							model.priors[1].cpu()

							set_seed(0     )
							model.priors[2].cuda()
							UpperCAmelCase_=   model._sample(_snake_case						,  _snake_case						,  [2]						,  sample_length=60 * model.priors[2].raw_to_tokens						,  save_results=_snake_case     )
							torch.testing.assert_allclose(zs[2][0].cpu()						,  torch.tensor(self.EXPECTED_GPU_OUTPUTS_0     )     )




	@slow
	def   _SCREAMING_SNAKE_CASE		(	self	:						Any     )			-> int:
							UpperCAmelCase_=   """ArthurZ/jukebox_prior_0"""
							UpperCAmelCase_=   JukeboxPrior.from_pretrained(_snake_case						,  min_duration=0     ).eval().half().to("""cuda"""     )

							UpperCAmelCase_=   self.prepare_inputs(_snake_case     )[0].cuda()
							UpperCAmelCase_=   model.get_metadata(_snake_case						,  0						,  7_680						,  0     )
							set_seed(0     )
							UpperCAmelCase_=   model.sample(1						,  metadata=_snake_case						,  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
import sys
import time

from . import transposition_cipher as trans_cipher

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      'Prehistoric Men.txt'
 lowerCAmelCase  							=      'Output.txt'
 lowerCAmelCase  							=      int(input('Enter key: '       )       )
 lowerCAmelCase  							=      input('Encrypt/Decrypt [e/d]: '       )

 if not os.path.exists(_UpperCAmelCase       ):
  print(F'File {input_file} does not exist. Quitting...'       )
  sys.exit()
 if os.path.exists(_UpperCAmelCase       ):
  print(F'Overwrite {output_file}? [y/n]'       )
  lowerCAmelCase  							=      input('> '       )
  if not response.lower().startswith('y'       ):
   sys.exit()

 lowerCAmelCase  							=      time.time()
 if mode.lower().startswith('e'       ):
  with open(_UpperCAmelCase       ) as f:
   lowerCAmelCase  							=      f.read()
  lowerCAmelCase  							=      trans_cipher.encrypt_message(_UpperCAmelCase   ,			_UpperCAmelCase       )
 elif mode.lower().startswith('d'       ):
  with open(_UpperCAmelCase       ) as f:
   lowerCAmelCase  							=      f.read()
  lowerCAmelCase  							=      trans_cipher.decrypt_message(_UpperCAmelCase   ,			_UpperCAmelCase       )

 with open(_UpperCAmelCase   ,			'w'       ) as output_obj:
  output_obj.write(_UpperCAmelCase       )

 lowerCAmelCase  							=      round(time.time() - start_time   ,			2       )
 print(('Done (', total_time, 'seconds )')       )


if __name__ == "__main__":
       main()





