





'''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 				lowerCAmelCase_						(						unittest.TestCase						):
 __lowerCamelCase  :							Optional[int]    					=   (JukeboxModel,) if is_torch_available() else ()
 __lowerCamelCase  :							Optional[int]    					=   "openai/jukebox-1b-lyrics"
 __lowerCamelCase  :							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
 __lowerCamelCase  :							Tuple    					=   [
     1_864, 1_536, 1_213, 1_870, 1_357, 1_536, 519, 880, 1_323, 789, 1_082, 534,
     1_000, 1_445, 1_105, 1_130, 967, 515, 1_434, 1_620, 534, 1_495, 283, 1_445,
     333, 1_307, 539, 1_631, 1_528, 375, 1_434, 673, 627, 710, 778, 1_883,
     1_405, 1_276, 1_455, 1_228
 ]

 __lowerCamelCase  :							Any    					=   [
     1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
     653, 653, 653, 653, 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  :							Optional[int]    					=   [
     1_125, 1_751, 697, 1_776, 1_141, 1_476, 391, 697, 1_125, 684, 867, 416,
     844, 1_372, 1_274, 717, 1_274, 844, 1_299, 1_419, 697, 1_370, 317, 1_125,
     191, 1_440, 1_370, 1_440, 1_370, 282, 1_621, 1_370, 368, 349, 867, 1_872,
     1_262, 869, 1_728, 747
 ]
 __lowerCamelCase  :							Tuple    					=   [
     416, 416, 1_125, 1_125, 416, 416, 416, 416, 416, 416, 416, 416,
     416, 416, 416, 416, 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  :							List[Any]    					=   [
     1_755, 842, 307, 1_843, 1_022, 1_395, 234, 1_554, 806, 739, 1_022, 442,
     616, 556, 268, 1_499, 933, 457, 1_440, 1_837, 755, 985, 308, 902,
     293, 1_443, 1_671, 1_141, 1_533, 555, 1_562, 1_061, 287, 417, 1_022, 2_008,
     1_186, 1_015, 1_777, 268
 ]
 __lowerCamelCase  :							str    					=   [
     854, 842, 1_353, 114, 1_353, 842, 185, 842, 185, 114, 591, 842,
     185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842,
     591, 842, 1_353, 842, 185, 842, 591, 842, 591, 114, 591, 842,
     185, 842, 591, 89
 ]

 __lowerCamelCase  :							List[Any]    					=   [1_058_304, 0, 786_432, 7_169, 507, 76, 27, 40, 30, 76]

 __lowerCamelCase  :							Optional[int]    					=   [
     390, 1_160, 1_002, 1_907, 1_788, 1_788, 1_788, 1_907, 1_002, 1_002, 1_854, 1_002,
     1_002, 1_002, 1_002, 1_002, 1_002, 1_160, 1_160, 1_606, 596, 596, 1_160, 1_002,
     1_516, 596, 1_002, 1_002, 1_002, 1_907, 1_788, 1_788, 1_788, 1_854, 1_788, 1_907,
     1_907, 1_788, 596, 1_626
 ]
 __lowerCamelCase  :							Dict    					=   [
     1_236, 1_668, 1_484, 1_920, 1_848, 1_409, 139, 864, 1_828, 1_272, 1_599, 824,
     1_672, 139, 555, 1_484, 824, 1_920, 555, 596, 1_579, 1_599, 1_231, 1_599,
     1_637, 1_407, 212, 824, 1_599, 116, 1_433, 824, 258, 1_599, 1_433, 1_895,
     1_063, 1_433, 1_433, 1_599
 ]
 __lowerCamelCase  :							Dict    					=   [
     1_684, 1_873, 1_119, 1_189, 395, 611, 1_901, 972, 890, 1_337, 1_392, 1_927,
     96, 972, 672, 780, 1_119, 890, 158, 771, 1_073, 1_927, 353, 1_331,
     1_269, 1_459, 1_333, 1_645, 812, 1_577, 1_337, 606, 353, 981, 1_466, 619,
     197, 391, 302, 1_930
 ]
 __lowerCamelCase  :							Optional[Any]    					=   [
     390, 1_160, 1_002, 1_907, 1_788, 1_788, 1_788, 1_907, 1_002, 1_002, 1_854, 1_002,
     1_002, 1_002, 1_002, 1_002, 1_002, 1_160, 1_160, 1_606, 596, 596, 1_160, 1_002,
     1_516, 596, 1_002, 1_002, 1_002, 1_907, 1_788, 1_788, 1_788, 1_854, 1_788, 1_907,
     1_907, 1_788, 596, 1_626
 ]
 __lowerCamelCase  :							Dict    					=   [
     -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  :							Tuple    					=   [
     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  :							Optional[Any]    					=   [
     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  :							Union[str, Any]    					=   [
     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 							_snake_case				(		self						)      ->							Tuple:
     _lowerCAmelCase		     = JukeboxTokenizer.from_pretrained(self.model_id						)
     _lowerCAmelCase		     = tokenizer(**self.metas						)["input_ids"]
     return tokens




 @slow
 def 							_snake_case				(		self						)      ->							Optional[int]:
     _lowerCAmelCase		     = JukeboxModel.from_pretrained(self.model_id     ,       min_duration=0						).eval()
     _lowerCAmelCase		     = self.prepare_inputs()

     set_seed(0						)
     _lowerCAmelCase		     = [torch.zeros(1     ,       0     ,       dtype=torch.long						).cpu() for _ in range(3						)]
     _lowerCAmelCase		     = model._sample(_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						)
     _lowerCAmelCase		     = 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						)
     _lowerCAmelCase		     = 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 							_snake_case				(		self						)      ->							Tuple:
     _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(_lowerCAmelCase     ,       start=_lowerCAmelCase     ,       end=start + top_prior.n_ctx						)
     _lowerCAmelCase		     = top_prior.get_metadata(labels[0].clone()     ,       _lowerCAmelCase     ,       1058304     ,       0						)

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

     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase		     = 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 							_snake_case				(		self						)      ->							int:
     _lowerCAmelCase		     = False

     _lowerCAmelCase		     = JukeboxModel.from_pretrained(self.model_id     ,       min_duration=0						).eval()
     set_seed(0						)
     _lowerCAmelCase		     = torch.rand((1, 5120, 1)						)
     _lowerCAmelCase		     = list(self.prepare_inputs()						)

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

     _lowerCAmelCase		     = torch.cat((zs[0], torch.zeros(1     ,       2048 - zs[0].shape[-1]						))     ,       dim=-1						).long()
     _lowerCAmelCase		     = [upper_a, model.vqvae.encode(_lowerCAmelCase     ,       start_level=1     ,       bs_chunks=waveform.shape[0]						)[0], None]
     _lowerCAmelCase		     = 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						)						)

     _lowerCAmelCase		     = torch.cat((zs[1], torch.zeros(1     ,       2048 - zs[1].shape[-1]						))     ,       dim=-1						).long()
     _lowerCAmelCase		     = [upper_a, upper_a, model.vqvae.encode(_lowerCAmelCase     ,       start_level=0     ,       bs_chunks=waveform.shape[0]						)[0]]
     _lowerCAmelCase		     = 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 							_snake_case				(		self						)      ->							Union[str, Any]:
     _lowerCAmelCase		     = JukeboxModel.from_pretrained(self.model_id     ,       min_duration=0						).eval()
     set_seed(0						)
     _lowerCAmelCase		     = torch.rand((1, 5120, 1)						)
     with torch.no_grad():
         _lowerCAmelCase		     = 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():
         _lowerCAmelCase		     = 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 				lowerCAmelCase_						(						unittest.TestCase						):
 __lowerCamelCase  :							List[Any]    					=   (JukeboxModel,) if is_torch_available() else ()
 __lowerCamelCase  :							int    					=   "openai/jukebox-5b-lyrics"
 __lowerCamelCase  :							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
 __lowerCamelCase  :							Union[str, Any]    					=   [
     1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
     653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
     653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
     653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
     1_489, 1_489, 1_489, 1_489, 1_150, 1_853, 1_509, 1_150, 1_357, 1_509, 6, 1_272
 ]
 __lowerCamelCase  :							Union[str, Any]    					=   [
     1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
     653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
     653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
     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  :							Dict    					=   [
     1_125, 416, 1_125, 1_125, 1_125, 1_125, 1_125, 416, 416, 416, 416, 416,
     416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
     416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
     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  :							Any    					=   [
     416, 416, 1_125, 1_125, 416, 416, 416, 416, 416, 416, 416, 416, 416,
     416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
     416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 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  :							List[str]    					=   [
     1_755, 1_061, 234, 1_755, 1_061, 1_755, 185, 290, 307, 307, 616, 616,
     616, 616, 616, 616, 307, 290, 417, 1_755, 234, 1_755, 185, 290,
     290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1_755,
     234, 234, 1_755, 234, 185, 185, 307, 616, 616, 616, 616, 290,
     1_755, 1_755, 1_755, 234, 234, 1_755, 1_572, 290, 307, 616, 34, 616
 ]
 __lowerCamelCase  :							List[str]    					=   [
     854, 842, 1_353, 114, 1_353, 842, 185, 842, 185, 114, 591, 842, 185,
     417, 185, 842, 307, 842, 591, 842, 185, 842, 185, 842, 591, 842,
     1_353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591,
     89, 591, 842, 591, 842, 591, 417, 1_372, 842, 1_372, 842, 34, 842,
     185, 89, 591, 842, 185, 842, 591, 632
 ]

 __lowerCamelCase  :							Optional[Any]    					=   [
     1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
     653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
     653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
     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  :							Any    					=   [
     1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
     653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
     653, 653, 653, 653, 653, 653, 653, 1_853, 1_177, 1_536, 1_228,
     710, 475, 1_489, 1_229, 1_224, 231, 1_224, 252, 1_434, 653, 475,
     1_106, 1_877, 1_599, 1_228, 1_600, 1_683, 1_182, 1_853, 475, 1_864,
     252, 1_229, 1_434, 2_001
 ]

 __lowerCamelCase  :							str    					=   [
     1_125, 1_125, 416, 1_125, 1_125, 416, 1_125, 1_125, 416, 416, 1_125, 416,
     416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
     416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
     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  :							Dict    					=   [
     491, 1_755, 34, 1_613, 1_755, 417, 992, 1_613, 222, 842, 1_353, 1_613,
     844, 632, 185, 1_613, 844, 632, 185, 1_613, 185, 842, 677, 1_613,
     185, 114, 1_353, 1_613, 307, 89, 844, 1_613, 307, 1_332, 234, 1_979,
     307, 89, 1_353, 616, 34, 842, 185, 842, 34, 842, 185, 842,
     307, 114, 185, 89, 34, 1_268, 185, 89, 34, 842, 185, 89
 ]




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Optional[int]:
     _lowerCAmelCase		     = JukeboxTokenizer.from_pretrained(_lowerCAmelCase						)
     _lowerCAmelCase		     = tokenizer(**self.metas						)["input_ids"]
     return tokens




 @slow
 def 							_snake_case				(		self						)      ->							List[str]:
     _lowerCAmelCase		     = JukeboxModel.from_pretrained(self.model_id     ,       min_duration=0						).eval()
     _lowerCAmelCase		     = self.prepare_inputs(self.model_id						)

     set_seed(0						)
     _lowerCAmelCase		     = [torch.zeros(1     ,       0     ,       dtype=torch.long						).cpu() for _ in range(3						)]
     _lowerCAmelCase		     = model._sample(_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						)
     _lowerCAmelCase		     = 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						)
     _lowerCAmelCase		     = 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 							_snake_case				(		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(_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()
     _lowerCAmelCase		     = 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()
     _lowerCAmelCase		     = 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 							_snake_case				(		self						)      ->							Optional[int]:
     _lowerCAmelCase		     = "ArthurZ/jukebox_prior_0"
     _lowerCAmelCase		     = JukeboxPrior.from_pretrained(_lowerCAmelCase     ,       min_duration=0						).eval().half().to("cuda"						)

     _lowerCAmelCase		     = self.prepare_inputs(_lowerCAmelCase						)[0].cuda()
     _lowerCAmelCase		     = model.get_metadata(_lowerCAmelCase     ,       0     ,       7680     ,       0						)
     set_seed(0						)
     _lowerCAmelCase		     = 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 unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
       import os

       # The slow tests are often failing with OOM error on GPU
       # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
       # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
       _SCREAMING_SNAKE_CASE			=							"platform"

       import jax
       import jax.numpy as jnp

       from transformers import AutoTokenizer
       from transformers.models.mbart.modeling_flax_mbart import (
           FlaxMBartForConditionalGeneration,
           FlaxMBartForQuestionAnswering,
           FlaxMBartForSequenceClassification,
           FlaxMBartModel,
           shift_tokens_right,
       )


def 		__a(SCREAMING_SNAKE_CASE_					:   Tuple						,    SCREAMING_SNAKE_CASE_					:   Union[str, Any]						,    SCREAMING_SNAKE_CASE_					:   Optional[int]=None						,    SCREAMING_SNAKE_CASE_					:   int=None						,    SCREAMING_SNAKE_CASE_					:   int=None						,    SCREAMING_SNAKE_CASE_					:   Union[str, Any]=None						,    SCREAMING_SNAKE_CASE_					:   Any=None						,    SCREAMING_SNAKE_CASE_					:   Tuple=None						,    ):



    '''simple docstring'''





    if attention_mask is None:
        _lowerCAmelCase		     = np.where(input_ids != config.pad_token_id						,    1						,    0      )
    if decoder_attention_mask is None:
        _lowerCAmelCase		     = np.where(decoder_input_ids != config.pad_token_id						,    1						,    0      )
    if head_mask is None:
        _lowerCAmelCase		     = np.ones((config.encoder_layers, config.encoder_attention_heads)      )
    if decoder_head_mask is None:
        _lowerCAmelCase		     = np.ones((config.decoder_layers, config.decoder_attention_heads)      )
    if cross_attn_head_mask is None:
        _lowerCAmelCase		     = np.ones((config.decoder_layers, config.decoder_attention_heads)      )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
    }

class 				lowerCAmelCase_						(						unittest.TestCase						):




 def __init__(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase=13     ,       _lowerCAmelCase=7     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=False     ,       _lowerCAmelCase=99     ,       _lowerCAmelCase=16     ,       _lowerCAmelCase=2     ,       _lowerCAmelCase=4     ,       _lowerCAmelCase=4     ,       _lowerCAmelCase="gelu"     ,       _lowerCAmelCase=0.1     ,       _lowerCAmelCase=0.1     ,       _lowerCAmelCase=32     ,       _lowerCAmelCase=2     ,       _lowerCAmelCase=1     ,       _lowerCAmelCase=0     ,       _lowerCAmelCase=2     ,       _lowerCAmelCase=0.02     ,       )      ->							List[Any]:
     _lowerCAmelCase		     = parent
     _lowerCAmelCase		     = batch_size
     _lowerCAmelCase		     = seq_length
     _lowerCAmelCase		     = is_training
     _lowerCAmelCase		     = use_labels
     _lowerCAmelCase		     = vocab_size
     _lowerCAmelCase		     = hidden_size
     _lowerCAmelCase		     = num_hidden_layers
     _lowerCAmelCase		     = num_attention_heads
     _lowerCAmelCase		     = intermediate_size
     _lowerCAmelCase		     = hidden_act
     _lowerCAmelCase		     = hidden_dropout_prob
     _lowerCAmelCase		     = attention_probs_dropout_prob
     _lowerCAmelCase		     = max_position_embeddings
     _lowerCAmelCase		     = eos_token_id
     _lowerCAmelCase		     = pad_token_id
     _lowerCAmelCase		     = bos_token_id
     _lowerCAmelCase		     = decoder_start_token_id
     _lowerCAmelCase		     = initializer_range




 def 							_snake_case				(		self						)      ->							str:
     _lowerCAmelCase		     = np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,       self.vocab_size						)     ,       3     ,       self.vocab_size						)
     _lowerCAmelCase		     = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,       dtype=np.intaa						))     ,       -1						)

     _lowerCAmelCase		     = shift_tokens_right(_lowerCAmelCase     ,       1						)

     _lowerCAmelCase		     = MBartConfig(
         vocab_size=self.vocab_size     ,       d_model=self.hidden_size     ,       encoder_layers=self.num_hidden_layers     ,       decoder_layers=self.num_hidden_layers     ,       encoder_attention_heads=self.num_attention_heads     ,       decoder_attention_heads=self.num_attention_heads     ,       encoder_ffn_dim=self.intermediate_size     ,       decoder_ffn_dim=self.intermediate_size     ,       dropout=self.hidden_dropout_prob     ,       attention_dropout=self.attention_probs_dropout_prob     ,       max_position_embeddings=self.max_position_embeddings     ,       eos_token_id=self.eos_token_id     ,       bos_token_id=self.bos_token_id     ,       pad_token_id=self.pad_token_id     ,       decoder_start_token_id=self.decoder_start_token_id     ,       initializer_range=self.initializer_range     ,       use_cache=_lowerCAmelCase     ,       )
     _lowerCAmelCase		     = prepare_mbart_inputs_dict(_lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)
     return config, inputs_dict




 def 							_snake_case				(		self						)      ->							int:
     _lowerCAmelCase     ,     _lowerCAmelCase		     = self.prepare_config_and_inputs()
     return config, inputs_dict




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Optional[Any]:
     _lowerCAmelCase		     = 20
     _lowerCAmelCase		     = model_class_name(_lowerCAmelCase						)

     _lowerCAmelCase		     = model.encode(inputs_dict["input_ids"]						)

     _lowerCAmelCase     ,     _lowerCAmelCase		     = (
         inputs_dict["decoder_input_ids"],
         inputs_dict["decoder_attention_mask"],
     )

     _lowerCAmelCase		     = model.init_cache(decoder_input_ids.shape[0]     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)
     _lowerCAmelCase		     = jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,       dtype="i4"						)

     _lowerCAmelCase		     = jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]     ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,       )
     _lowerCAmelCase		     = model.decode(
         decoder_input_ids[:, :-1]     ,       _lowerCAmelCase     ,       decoder_attention_mask=_lowerCAmelCase     ,       past_key_values=_lowerCAmelCase     ,       decoder_position_ids=_lowerCAmelCase     ,       )

     _lowerCAmelCase		     = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,       dtype="i4"						)
     _lowerCAmelCase		     = model.decode(
         decoder_input_ids[:, -1:]     ,       _lowerCAmelCase     ,       decoder_attention_mask=_lowerCAmelCase     ,       past_key_values=outputs_cache.past_key_values     ,       decoder_position_ids=_lowerCAmelCase     ,       )

     _lowerCAmelCase		     = model.decode(_lowerCAmelCase     ,       _lowerCAmelCase						)

     _lowerCAmelCase		     = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
     self.parent.assertTrue(diff < 1E-3     ,       msg=f'''Max diff is {diff}'''						)





 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Union[str, Any]:
     _lowerCAmelCase		     = 20
     _lowerCAmelCase		     = model_class_name(_lowerCAmelCase						)

     _lowerCAmelCase		     = model.encode(inputs_dict["input_ids"]						)

     _lowerCAmelCase     ,     _lowerCAmelCase		     = (
         inputs_dict["decoder_input_ids"],
         inputs_dict["decoder_attention_mask"],
     )

     _lowerCAmelCase		     = jnp.concatenate(
         [
             decoder_attention_mask,
             jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
         ]     ,       axis=-1     ,       )

     _lowerCAmelCase		     = model.init_cache(decoder_input_ids.shape[0]     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)
     _lowerCAmelCase		     = jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]     ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,       )

     _lowerCAmelCase		     = model.decode(
         decoder_input_ids[:, :-1]     ,       _lowerCAmelCase     ,       decoder_attention_mask=_lowerCAmelCase     ,       past_key_values=_lowerCAmelCase     ,       decoder_position_ids=_lowerCAmelCase     ,       )
     _lowerCAmelCase		     = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,       dtype="i4"						)
     _lowerCAmelCase		     = model.decode(
         decoder_input_ids[:, -1:]     ,       _lowerCAmelCase     ,       past_key_values=outputs_cache.past_key_values     ,       decoder_attention_mask=_lowerCAmelCase     ,       decoder_position_ids=_lowerCAmelCase     ,       )

     _lowerCAmelCase		     = model.decode(_lowerCAmelCase     ,       _lowerCAmelCase     ,       decoder_attention_mask=_lowerCAmelCase						)

     _lowerCAmelCase		     = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
     self.parent.assertTrue(diff < 1E-3     ,       msg=f'''Max diff is {diff}'''						)







@require_flax
class 				lowerCAmelCase_						(						unittest.TestCase						):
 __lowerCamelCase  :							Optional[int]    					=   99




 def 							_snake_case				(		self						)      ->							Tuple:
     _lowerCAmelCase		     = np.array(
         [
             [71, 82, 18, 33, 46, 91, 2],
             [68, 34, 26, 58, 30, 82, 2],
             [5, 97, 17, 39, 94, 40, 2],
             [76, 83, 94, 25, 70, 78, 2],
             [87, 59, 41, 35, 48, 66, 2],
             [55, 13, 16, 58, 5, 2, 1],  # note padding
             [64, 27, 31, 51, 12, 75, 2],
             [52, 64, 86, 17, 83, 39, 2],
             [48, 61, 9, 24, 71, 82, 2],
             [26, 1, 60, 48, 22, 13, 2],
             [21, 5, 62, 28, 14, 76, 2],
             [45, 98, 37, 86, 59, 48, 2],
             [70, 70, 50, 9, 28, 0, 2],
         ]     ,       dtype=np.intaa     ,       )

     _lowerCAmelCase		     = input_ids.shape[0]
     _lowerCAmelCase		     = MBartConfig(
         vocab_size=self.vocab_size     ,       d_model=24     ,       encoder_layers=2     ,       decoder_layers=2     ,       encoder_attention_heads=2     ,       decoder_attention_heads=2     ,       encoder_ffn_dim=32     ,       decoder_ffn_dim=32     ,       max_position_embeddings=48     ,       eos_token_id=2     ,       pad_token_id=1     ,       bos_token_id=0     ,       )
     return config, input_ids, batch_size




 def 							_snake_case				(		self						)      ->							Any:
     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase		     = self._get_config_and_data()
     _lowerCAmelCase		     = FlaxMBartForSequenceClassification(_lowerCAmelCase						)
     _lowerCAmelCase		     = model(input_ids=_lowerCAmelCase     ,       decoder_input_ids=_lowerCAmelCase						)
     _lowerCAmelCase		     = (batch_size, config.num_labels)
     self.assertEqual(outputs["logits"].shape     ,       _lowerCAmelCase						)




 def 							_snake_case				(		self						)      ->							Optional[int]:
     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase		     = self._get_config_and_data()
     _lowerCAmelCase		     = FlaxMBartForQuestionAnswering(_lowerCAmelCase						)
     _lowerCAmelCase		     = model(input_ids=_lowerCAmelCase						)

     self.assertEqual(outputs["start_logits"].shape     ,       input_ids.shape						)
     self.assertEqual(outputs["end_logits"].shape     ,       input_ids.shape						)




 def 							_snake_case				(		self						)      ->							List[Any]:
     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase		     = self._get_config_and_data()
     _lowerCAmelCase		     = FlaxMBartForConditionalGeneration(_lowerCAmelCase						)
     _lowerCAmelCase		     = lm_model(input_ids=_lowerCAmelCase						)
     _lowerCAmelCase		     = (batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs["logits"].shape     ,       _lowerCAmelCase						)




 def 							_snake_case				(		self						)      ->							str:
     _lowerCAmelCase		     = MBartConfig(
         vocab_size=self.vocab_size     ,       d_model=14     ,       encoder_layers=2     ,       decoder_layers=2     ,       encoder_attention_heads=2     ,       decoder_attention_heads=2     ,       encoder_ffn_dim=8     ,       decoder_ffn_dim=8     ,       max_position_embeddings=48     ,       )
     _lowerCAmelCase		     = FlaxMBartForConditionalGeneration(_lowerCAmelCase						)
     _lowerCAmelCase		     = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]     ,       dtype=np.intaa						)
     _lowerCAmelCase		     = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]     ,       dtype=np.intaa						)
     _lowerCAmelCase		     = lm_model(input_ids=_lowerCAmelCase     ,       decoder_input_ids=_lowerCAmelCase						)
     _lowerCAmelCase		     = (*summary.shape, config.vocab_size)
     self.assertEqual(outputs["logits"].shape     ,       _lowerCAmelCase						)





 def 							_snake_case				(		self						)      ->							Any:
     _lowerCAmelCase		     = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]     ,       dtype=np.intaa						)
     _lowerCAmelCase		     = shift_tokens_right(_lowerCAmelCase     ,       1						)
     _lowerCAmelCase		     = np.equal(_lowerCAmelCase     ,       1						).astype(np.floataa						).sum()
     _lowerCAmelCase		     = np.equal(_lowerCAmelCase     ,       1						).astype(np.floataa						).sum()
     self.assertEqual(shifted.shape     ,       input_ids.shape						)
     self.assertEqual(_lowerCAmelCase     ,       n_pad_before - 1						)
     self.assertTrue(np.equal(shifted[:, 0]     ,       2						).all()						)







@require_flax
class 				lowerCAmelCase_						(						__magic_name__  ,unittest.TestCase  ,__magic_name__						):
 __lowerCamelCase  :							List[Any]    					=   True
 __lowerCamelCase  :							Tuple    					=   (
     (
         FlaxMBartModel,
         FlaxMBartForConditionalGeneration,
         FlaxMBartForSequenceClassification,
         FlaxMBartForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )
 __lowerCamelCase  :							str    					=   (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




 def 							_snake_case				(		self						)      ->							Tuple:
     _lowerCAmelCase		     = FlaxMBartModelTester(self						)




 def 							_snake_case				(		self						)      ->							Optional[int]:
     _lowerCAmelCase     ,     _lowerCAmelCase		     = self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(_lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)




 def 							_snake_case				(		self						)      ->							str:
     _lowerCAmelCase     ,     _lowerCAmelCase		     = self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward_with_attn_mask(_lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)




 def 							_snake_case				(		self						)      ->							Optional[int]:
     _lowerCAmelCase     ,     _lowerCAmelCase		     = self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__						):
             _lowerCAmelCase		     = self._prepare_for_class(_lowerCAmelCase     ,       _lowerCAmelCase						)
             _lowerCAmelCase		     = model_class(_lowerCAmelCase						)

             @jax.jit
             def encode_jitted(_lowerCAmelCase     ,       _lowerCAmelCase=None     ,       **_lowerCAmelCase						):
                 return model.encode(input_ids=_lowerCAmelCase     ,       attention_mask=_lowerCAmelCase						)

             with self.subTest("JIT Enabled"						):
                 _lowerCAmelCase		     = encode_jitted(**_lowerCAmelCase						).to_tuple()

             with self.subTest("JIT Disabled"						):
                 with jax.disable_jit():
                     _lowerCAmelCase		     = encode_jitted(**_lowerCAmelCase						).to_tuple()

             self.assertEqual(len(_lowerCAmelCase						)     ,       len(_lowerCAmelCase						)						)
             for jitted_output, output in zip(_lowerCAmelCase     ,       _lowerCAmelCase						):
                 self.assertEqual(jitted_output.shape     ,       output.shape						)




 def 							_snake_case				(		self						)      ->							str:
     _lowerCAmelCase     ,     _lowerCAmelCase		     = self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__						):
             _lowerCAmelCase		     = model_class(_lowerCAmelCase						)
             _lowerCAmelCase		     = model.encode(inputs_dict["input_ids"]     ,       inputs_dict["attention_mask"]						)

             _lowerCAmelCase		     = {
                 "decoder_input_ids": inputs_dict["decoder_input_ids"],
                 "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                 "encoder_outputs": encoder_outputs,
             }

             @jax.jit
             def decode_jitted(_lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						):
                 return model.decode(
                     decoder_input_ids=_lowerCAmelCase     ,       decoder_attention_mask=_lowerCAmelCase     ,       encoder_outputs=_lowerCAmelCase     ,       )

             with self.subTest("JIT Enabled"						):
                 _lowerCAmelCase		     = decode_jitted(**_lowerCAmelCase						).to_tuple()

             with self.subTest("JIT Disabled"						):
                 with jax.disable_jit():
                     _lowerCAmelCase		     = decode_jitted(**_lowerCAmelCase						).to_tuple()

             self.assertEqual(len(_lowerCAmelCase						)     ,       len(_lowerCAmelCase						)						)
             for jitted_output, output in zip(_lowerCAmelCase     ,       _lowerCAmelCase						):
                 self.assertEqual(jitted_output.shape     ,       output.shape						)





 @slow
 def 							_snake_case				(		self						)      ->							int:
     for model_class_name in self.all_model_classes:
         _lowerCAmelCase		     = model_class_name.from_pretrained("facebook/mbart-large-cc25"     ,       from_pt=_lowerCAmelCase						)
         # FlaxMBartForSequenceClassification expects eos token in input_ids
         _lowerCAmelCase		     = np.ones((1, 1)						) * model.config.eos_token_id
         _lowerCAmelCase		     = model(_lowerCAmelCase						)
         self.assertIsNotNone(_lowerCAmelCase						)







@require_flax
@require_sentencepiece
@require_tokenizers
class 				lowerCAmelCase_						(						unittest.TestCase						):
 __lowerCamelCase  :							Tuple    					=   [
     " UN Chief Says There Is No Military Solution in Syria",
 ]
 __lowerCamelCase  :							Optional[int]    					=   [
     "Şeful ONU declară că nu există o soluţie militară în Siria",
 ]
 __lowerCamelCase  :							Union[str, Any]    					=   "facebook/mbart-large-en-ro"




 @cached_property
 def 							_snake_case				(		self						)      ->							Union[str, Any]:
     return AutoTokenizer.from_pretrained(self.model_name						)




 @cached_property
 def 							_snake_case				(		self						)      ->							Union[str, Any]:
     _lowerCAmelCase		     = FlaxMBartForConditionalGeneration.from_pretrained(self.model_name     ,       from_pt=_lowerCAmelCase						)
     return model




 def 							_snake_case				(		self     ,       **_lowerCAmelCase						)      ->							int:
     _lowerCAmelCase		     = self.translate_src_text(**_lowerCAmelCase						)
     self.assertListEqual(self.expected_text     ,       _lowerCAmelCase						)




 def 							_snake_case				(		self     ,       **_lowerCAmelCase						)      ->							Optional[Any]:
     _lowerCAmelCase		     = self.tokenizer(self.src_text     ,       **_lowerCAmelCase     ,       return_tensors="np"						)
     _lowerCAmelCase		     = self.model.generate(
         model_inputs.input_ids     ,       attention_mask=model_inputs.attention_mask     ,       decoder_start_token_id=self.tokenizer.lang_code_to_id["ro_RO"]     ,       early_stopping=_lowerCAmelCase     ,       num_beams=2     ,       ).sequences
     _lowerCAmelCase		     = self.tokenizer.batch_decode(_lowerCAmelCase     ,       skip_special_tokens=_lowerCAmelCase						)
     return generated_words





 @slow
 def 							_snake_case				(		self						)      ->							Optional[Any]:
     self._assert_generated_batch_equal_expected()


