
'''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      ):
 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_492, -0.0_524, -0.0_565, -0.0_640, -0.0_686, -0.0_684, -0.0_677, -0.0_664,
     -0.0_605, -0.0_490, -0.0_330, -0.0_168, -0.0_083, -0.0_075, -0.0_051, 0.0_025,
     0.0_136, 0.0_261, 0.0_386, 0.0_497, 0.0_580, 0.0_599, 0.0_583, 0.0_614,
     0.0_740, 0.0_889, 0.1_023, 0.1_162, 0.1_211, 0.1_212, 0.1_251, 0.1_336,
     0.1_502, 0.1_686, 0.1_883, 0.2_148, 0.2_363, 0.2_458, 0.2_507, 0.2_531
 ]
 lowerCamelCase												=[
     0.0_256, -0.0_544, 0.1_600, -0.0_032, 0.1_066, 0.0_825, -0.0_013, 0.3_440,
     0.0_210, 0.0_412, -0.1_777, -0.0_892, -0.0_164, 0.0_285, -0.0_613, -0.0_617,
     -0.0_137, -0.0_201, -0.0_175, 0.0_215, -0.0_627, 0.0_520, -0.0_730, 0.0_970,
     -0.0_100, 0.0_442, -0.0_586, 0.0_207, -0.0_015, -0.0_082
 ]
 lowerCamelCase												=[
     0.0_415, 0.0_877, 0.0_022, -0.0_055, 0.0_751, 0.0_334, 0.0_324, -0.0_068,
     0.0_011, 0.0_017, -0.0_676, 0.0_655, -0.0_143, 0.0_399, 0.0_303, 0.0_743,
     -0.0_168, -0.0_394, -0.1_113, 0.0_124, 0.0_442, 0.0_267, -0.0_003, -0.1_536,
     -0.0_116, -0.1_837, -0.0_180, -0.1_026, -0.0_777, -0.0_456
 ]
 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 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   __A		:  str       				=		JukeboxTokenizer.from_pretrained(self.model_id							)
   __A		:  Dict       				=		tokenizer(**self.metas							)['''input_ids''']
   return tokens
 @slow
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   __A		:  Any       				=		JukeboxModel.from_pretrained(self.model_id					, min_duration=0							).eval()
   __A		:  str       				=		self.prepare_inputs()

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

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

   set_seed(0							)
   __A		:  Dict       				=		model._sample(a__					, a__					, [2]					, sample_length=40 * model.priors[2].raw_to_tokens					, save_results=a__							)
   self.assertIn(zs[2][0].detach().cpu().tolist()					, [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]							)
 @slow
 def 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





   __A		:  Dict       				=		False
   __A		:  List[str]       				=		JukeboxModel.from_pretrained(self.model_id					, min_duration=0							).eval()

   __A		:  int       				=		self.prepare_inputs()
   set_seed(0							)
   __A		:  Union[str, Any]       				=		[torch.zeros(1					, 0					, dtype=torch.long							) for _ in range(3							)]

   __A		:  str       				=		model.priors[0]
   __A		:  Optional[int]       				=		0
   __A		:  int       				=		top_prior.get_music_tokens_conds(a__					, start=a__					, end=start + top_prior.n_ctx							)
   __A		:  Optional[Any]       				=		top_prior.get_metadata(labels[0].clone()					, a__					, 1_05_83_04					, 0							)

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

   __A		:  str       				=		top_prior.get_cond(a__					, a__							)
   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 		lowercase_( self      :     str							):



   """simple docstring"""





   __A		:  List[str]       				=		False

   __A		:  Union[str, Any]       				=		JukeboxModel.from_pretrained(self.model_id					, min_duration=0							).eval()
   set_seed(0							)
   __A		:  Optional[int]       				=		torch.rand((1, 51_20, 1)							)
   __A		:  Optional[Any]       				=		list(self.prepare_inputs()							)

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

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

   __A		:  List[Any]       				=		torch.cat((zs[1], torch.zeros(1					, 20_48 - zs[1].shape[-1]							))					, dim=-1							).long()
   __A		:  str       				=		[upper_a, upper_a, model.vqvae.encode(a__					, start_level=0					, bs_chunks=waveform.shape[0]							)[0]]
   __A		:  Dict       				=		model._sample(
       a__					, a__					, sample_levels=[2]					, save_results=a__					, 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 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   __A		:  Union[str, Any]       				=		JukeboxModel.from_pretrained(self.model_id					, min_duration=0							).eval()
   set_seed(0							)
   __A		:  Union[str, Any]       				=		torch.rand((1, 51_20, 1)							)
   with torch.no_grad():
     __A		:  List[Any]       				=		model.vqvae.encode(a__					, 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		:  Optional[int]       				=		model.vqvae.decode(a__					, 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      ):
 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 		lowercase_( self      :     str					, lowerCamelCase      :     str							):



   """simple docstring"""





   __A		:  int       				=		JukeboxTokenizer.from_pretrained(a__							)
   __A		:  Tuple       				=		tokenizer(**self.metas							)['''input_ids''']
   return tokens
 @slow
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   __A		:  Any       				=		JukeboxModel.from_pretrained(self.model_id					, min_duration=0							).eval()
   __A		:  List[Any]       				=		self.prepare_inputs(self.model_id							)

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

   set_seed(0							)
   __A		:  List[Any]       				=		model._sample(a__					, a__					, [1]					, sample_length=60 * model.priors[1].raw_to_tokens					, save_results=a__							)
   self.assertIn(zs[1][0].detach().cpu().tolist()					, [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]							)

   set_seed(0							)
   __A		:  Tuple       				=		model._sample(a__					, a__					, [2]					, sample_length=60 * model.priors[2].raw_to_tokens					, save_results=a__							)
   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 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





   __A		:  Union[str, Any]       				=		JukeboxModel.from_pretrained(self.model_id					, min_duration=0							).eval()
   __A		:  Optional[Any]       				=		[i.cuda() for i in self.prepare_inputs(self.model_id							)]

   set_seed(0							)
   model.priors[0].cuda()
   __A		:  int       				=		[torch.zeros(1					, 0					, dtype=torch.long							).cuda() for _ in range(3							)]
   __A		:  Optional[int]       				=		model._sample(a__					, a__					, [0]					, sample_length=60 * model.priors[0].raw_to_tokens					, save_results=a__							)
   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		:  Optional[Any]       				=		model._sample(a__					, a__					, [1]					, sample_length=60 * model.priors[1].raw_to_tokens					, save_results=a__							)
   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		:  Dict       				=		model._sample(a__					, a__					, [2]					, sample_length=60 * model.priors[2].raw_to_tokens					, save_results=a__							)
   torch.testing.assert_allclose(zs[2][0].cpu()					, torch.tensor(self.EXPECTED_GPU_OUTPUTS_0							)							)






 @slow
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





   __A		:  Optional[int]       				=		'''ArthurZ/jukebox_prior_0'''
   __A		:  Any       				=		JukeboxPrior.from_pretrained(a__					, min_duration=0							).eval().half().to("""cuda"""							)

   __A		:  int       				=		self.prepare_inputs(a__							)[0].cuda()
   __A		:  Optional[Any]       				=		model.get_metadata(a__					, 0					, 76_80					, 0							)
   set_seed(0							)
   __A		:  str       				=		model.sample(1					, metadata=a__					, sample_tokens=60							)
   self.assertIn(outputs[0].cpu().tolist()					, [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]							)







from sklearn.metrics import mean_absolute_error

import datasets


a					:				Optional[Any]       =      '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

a					:				int       =      '\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'


a					:				Any       =      '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 "raw_values" : Returns a full set of errors in case of multioutput input.\n\n                 "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric("mae")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.5}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric("mae", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results)\n    {\'mae\': array([0.5, 1. ])}\n'

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,      _KWARGS_DESCRIPTION  )
class   lowerCamelCase						(	datasets.Metric  ):

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return datasets.MetricInfo(
            description=_DESCRIPTION						,    citation=_CITATION						,    inputs_description=_KWARGS_DESCRIPTION						,    features=datasets.Features(self._get_feature_types()       )						,    reference_urls=[
                '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html'''
            ]						,    )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        if self.config_name == "multilist":
         return {
             "predictions": datasets.Sequence(datasets.Value('''float'''       )       ),
             "references": datasets.Sequence(datasets.Value('''float'''       )       ),
         }
        else:
         return {
             "predictions": datasets.Value('''float'''       ),
             "references": datasets.Value('''float'''       ),
         }



       def 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__=None						,    a__="uniform_average"       ):






        '''simple docstring'''






        snake_case :			str							=   mean_absolute_error(a__						,    a__						,    sample_weight=a__						,    multioutput=a__       )

        return {"mae": mae_score}






