


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

       _UpperCamelCase     		=   [
           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
       ]

       _UpperCamelCase     		=   [
           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
       ]
       _UpperCamelCase     		=   [
           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
       ]

       _UpperCamelCase     		=   [
           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
       ]
       _UpperCamelCase     		=   [
           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
       ]

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

       _UpperCamelCase     		=   [
           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
       ]
       _UpperCamelCase     		=   [
           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
       ]
       _UpperCamelCase     		=   [
           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
       ]
       _UpperCamelCase     		=   [
           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
       ]
       _UpperCamelCase     		=   [
           -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
       ]
       _UpperCamelCase     		=   [
           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
       ]
       _UpperCamelCase     		=   [
           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
       ]
       _UpperCamelCase     		=   [
           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 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			str							=   JukeboxTokenizer.from_pretrained(self.model_id       )
        snake_case :			Dict							=   tokenizer(**self.metas       )['''input_ids''']
        return tokens

       @slow
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






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

        set_seed(0       )
        snake_case :			Any							=   [torch.zeros(1						,    0						,    dtype=torch.long       ).cpu() for _ in range(3       )]
        snake_case :			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       )
        snake_case :			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       )
        snake_case :			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 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






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

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

        snake_case :			str							=   model.priors[0]
        snake_case :			Optional[int]							=   0
        snake_case :			int							=   top_prior.get_music_tokens_conds(a__						,    start=a__						,    end=start + top_prior.n_ctx       )
        snake_case :			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       )

        snake_case	,       snake_case	,       snake_case :			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 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			List[str]							=   False

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

        snake_case :			Optional[int]							=   [model.vqvae.encode(a__						,    start_level=2						,    bs_chunks=waveform.shape[0]       )[0], None, None]
        snake_case :			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       )       )

        snake_case :			Dict							=   torch.cat((zs[0], torch.zeros(1						,    20_48 - zs[0].shape[-1]       ))						,    dim=-1       ).long()
        snake_case :			Tuple							=   [upper_a, model.vqvae.encode(a__						,    start_level=1						,    bs_chunks=waveform.shape[0]       )[0], None]
        snake_case :			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       )       )

        snake_case :			List[Any]							=   torch.cat((zs[1], torch.zeros(1						,    20_48 - zs[1].shape[-1]       ))						,    dim=-1       ).long()
        snake_case :			str							=   [upper_a, upper_a, model.vqvae.encode(a__						,    start_level=0						,    bs_chunks=waveform.shape[0]       )[0]]
        snake_case :			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 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Union[str, Any]							=   JukeboxModel.from_pretrained(self.model_id						,    min_duration=0       ).eval()
        set_seed(0       )
        snake_case :			Union[str, Any]							=   torch.rand((1, 51_20, 1)       )
        with torch.no_grad():
         snake_case :			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():
         snake_case :			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   lowerCamelCase						(	unittest.TestCase  ):
       _UpperCamelCase     		=   (JukeboxModel,) if is_torch_available() else ()
       _UpperCamelCase     		=   """openai/jukebox-5b-lyrics"""
       _UpperCamelCase     		=   {
           """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
       _UpperCamelCase     		=   [
           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
       ]
       _UpperCamelCase     		=   [
           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
       ]

       _UpperCamelCase     		=   [
           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
       ]
       _UpperCamelCase     		=   [
           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
       ]

       _UpperCamelCase     		=   [
           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
       ]
       _UpperCamelCase     		=   [
           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
       ]

       _UpperCamelCase     		=   [
           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
       ]
       _UpperCamelCase     		=   [
           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
       ]

       _UpperCamelCase     		=   [
           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
       ]
       _UpperCamelCase     		=   [
           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 					UpperCAmelCase					(      self						,    a__       ):






        '''simple docstring'''






        snake_case :			int							=   JukeboxTokenizer.from_pretrained(a__       )
        snake_case :			Tuple							=   tokenizer(**self.metas       )['''input_ids''']
        return tokens

       @slow
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






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

        set_seed(0       )
        snake_case :			int							=   [torch.zeros(1						,    0						,    dtype=torch.long       ).cpu() for _ in range(3       )]
        snake_case :			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       )
        snake_case :			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       )
        snake_case :			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 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






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

        set_seed(0       )
        model.priors[0].cuda()
        snake_case :			int							=   [torch.zeros(1						,    0						,    dtype=torch.long       ).cuda() for _ in range(3       )]
        snake_case :			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()
        snake_case :			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()
        snake_case :			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 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






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

        snake_case :			int							=   self.prepare_inputs(a__       )[0].cuda()
        snake_case :			Optional[Any]							=   model.get_metadata(a__						,    0						,    76_80						,    0       )
        set_seed(0       )
        snake_case :			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]       )










import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

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


if is_flax_available():
  import jax
  import jax.numpy as jnp
  import numpy as np

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
  import torch

@require_flax
class   lowerCamelCase						:

       def __init__(      self						,    a__						,    a__=14						,    a__=7						,    a__=True						,    a__=True						,    a__=True						,    a__=99						,    a__=32						,    a__=5						,    a__=4						,    a__=37						,    a__="gelu"						,    a__=0.1						,    a__=0.1						,    a__=5_12						,    a__=0.02						,    a__=None						,    ):






        '''simple docstring'''






        snake_case :			Any							=   parent
        snake_case :			str							=   batch_size
        snake_case :			str							=   seq_length
        snake_case :			str							=   is_training
        snake_case :			List[str]							=   use_input_mask
        snake_case :			str							=   use_labels
        snake_case :			Union[str, Any]							=   vocab_size
        snake_case :			Dict							=   d_model
        snake_case :			List[str]							=   num_hidden_layers
        snake_case :			Optional[int]							=   num_attention_heads
        snake_case :			Optional[Any]							=   ffn_dim
        snake_case :			Dict							=   activation_function
        snake_case :			List[str]							=   activation_dropout
        snake_case :			Any							=   attention_dropout
        snake_case :			str							=   max_position_embeddings
        snake_case :			Optional[int]							=   initializer_range
        snake_case :			Union[str, Any]							=   None
        snake_case :			int							=   0
        snake_case :			int							=   2
        snake_case :			Union[str, Any]							=   1

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Any							=   np.clip(ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size       )						,    3						,    self.vocab_size       )

        snake_case :			Union[str, Any]							=   None
        if self.use_input_mask:
         snake_case :			Tuple							=   random_attention_mask([self.batch_size, self.seq_length]       )

        snake_case :			Optional[Any]							=   XGLMConfig(
            vocab_size=self.vocab_size						,    d_model=self.hidden_size						,    num_layers=self.num_hidden_layers						,    attention_heads=self.num_attention_heads						,    ffn_dim=self.ffn_dim						,    activation_function=self.activation_function						,    activation_dropout=self.activation_dropout						,    attention_dropout=self.attention_dropout						,    max_position_embeddings=self.max_position_embeddings						,    initializer_range=self.initializer_range						,    use_cache=a__						,    bos_token_id=self.bos_token_id						,    eos_token_id=self.eos_token_id						,    pad_token_id=self.pad_token_id						,    )

        return (config, input_ids, input_mask)

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   self.prepare_config_and_inputs()
        snake_case	,       snake_case	,       snake_case :			List[str]							=   config_and_inputs
        snake_case :			List[str]							=   {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case	,       snake_case	,       snake_case :			List[str]							=   self.prepare_config_and_inputs()

        snake_case :			Tuple							=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
        snake_case :			Union[str, Any]							=   ids_tensor([self.batch_size, self.seq_length]						,    vocab_size=2       )

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
        )

       def 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__						,    a__       ):






        '''simple docstring'''






        snake_case :			Optional[Any]							=   20
        snake_case :			List[Any]							=   model_class_name(a__       )

        snake_case :			List[Any]							=   model.init_cache(input_ids.shape[0]						,    a__       )
        snake_case :			Optional[int]							=   jnp.ones((input_ids.shape[0], max_decoder_length)						,    dtype='''i4'''       )

        snake_case :			Optional[Any]							=   jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1       )[None, :]						,    (input_ids.shape[0], input_ids.shape[-1] - 1)       )
        snake_case :			Tuple							=   model(
            input_ids[:, :-1]						,    attention_mask=a__						,    past_key_values=a__						,    position_ids=a__						,    )

        snake_case :			Dict							=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,    dtype='''i4'''       )
        snake_case :			str							=   model(
            input_ids[:, -1:]						,    attention_mask=a__						,    past_key_values=outputs_cache.past_key_values						,    position_ids=a__						,    )

        snake_case :			Dict							=   model(a__       )

        snake_case :			Union[str, Any]							=   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 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__						,    a__       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   20
        snake_case :			Dict							=   model_class_name(a__       )

        snake_case :			Dict							=   jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])       )]						,    axis=-1						,    )

        snake_case :			List[str]							=   model.init_cache(input_ids.shape[0]						,    a__       )
        snake_case :			Optional[int]							=   jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1       )[None, :]						,    (input_ids.shape[0], input_ids.shape[-1] - 1)       )

        snake_case :			Optional[int]							=   model(
            input_ids[:, :-1]						,    attention_mask=a__						,    past_key_values=a__						,    position_ids=a__						,    )
        snake_case :			int							=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,    dtype='''i4'''       )
        snake_case :			Optional[int]							=   model(
            input_ids[:, -1:]						,    past_key_values=outputs_cache.past_key_values						,    attention_mask=a__						,    position_ids=a__						,    )

        snake_case :			Optional[Any]							=   model(a__						,    attention_mask=a__       )
        snake_case :			Optional[int]							=   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_sentencepiece
@require_flax
class   lowerCamelCase						(	SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      unittest.TestCase  ):
       _UpperCamelCase     		=   (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
       _UpperCamelCase     		=   (FlaxXGLMForCausalLM,) if is_flax_available() else ()

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Any							=   FlaxXGLMModelTester(self       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
         snake_case	,       snake_case	,       snake_case :			str							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward(a__						,    a__						,    a__						,    a__       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
         snake_case	,       snake_case	,       snake_case :			int							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward_with_attn_mask(
             a__						,    a__						,    a__						,    a__       )

       @slow
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Any							=   XGLMTokenizer.from_pretrained('''XGLM'''						,    padding_side='''left'''       )
        snake_case :			Optional[int]							=   tokenizer(['''Hello this is a long string''', '''Hey''']						,    return_tensors='''np'''						,    padding=a__						,    truncation=a__       )

        snake_case :			List[Any]							=   FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''       )
        snake_case :			Optional[int]							=   1
        snake_case :			str							=   False

        snake_case :			str							=   jax.jit(model.generate       )

        snake_case :			Dict							=   jit_generate(inputs['''input_ids''']						,    attention_mask=inputs['''attention_mask''']       ).sequences

        snake_case :			Optional[Any]							=   tokenizer.batch_decode(a__						,    skip_special_tokens=a__       )

        snake_case :			Tuple							=   [
            '''Hello this is a long string of questions, but I\'m not sure if I\'m''',
            '''Hey, I\'m a newbie to the forum and I\'''',
        ]

        self.assertListEqual(a__						,    a__       )

       @is_pt_flax_cross_test
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case	,       snake_case :			Union[str, Any]							=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__       ):
          # prepare inputs
          snake_case :			Union[str, Any]							=   self._prepare_for_class(a__						,    a__       )
          snake_case :			int							=   {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

          # load corresponding PyTorch class
          snake_case :			Any							=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
          snake_case :			Optional[Any]							=   getattr(a__						,    a__       )

          snake_case	,       snake_case :			Optional[Any]							=   pt_inputs['''input_ids'''].shape
          snake_case :			Tuple							=   np.random.randint(0						,    seq_length - 1						,    size=(batch_size,)       )
          for batch_idx, start_index in enumerate(a__       ):
           snake_case :			Optional[Any]							=   0
           snake_case :			Optional[int]							=   1
           snake_case :			Optional[Any]							=   0
           snake_case :			Optional[Any]							=   1
          snake_case :			Dict							=   pt_model_class(a__       ).eval()
          # Flax models don't use the `use_cache` option and cache is not returned as a default.
          # So we disable `use_cache` here for PyTorch model.
          snake_case :			Any							=   False
          snake_case :			str							=   model_class(a__						,    dtype=jnp.floataa       )
          snake_case :			Optional[int]							=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,    a__       )
          snake_case :			Optional[int]							=   fx_state

          with torch.no_grad():
           snake_case :			int							=   pt_model(**a__       ).to_tuple()

          snake_case :			Optional[int]							=   fx_model(**a__       ).to_tuple()
          self.assertEqual(len(a__       )						,    len(a__       )						,    '''Output lengths differ between Flax and PyTorch'''       )
          for fx_output, pt_output in zip(a__						,    a__       ):
           self.assert_almost_equals(fx_output[:, -1]						,    pt_output[:, -1].numpy()						,    4e-2       )

          with tempfile.TemporaryDirectory() as tmpdirname:
           pt_model.save_pretrained(a__       )
           snake_case :			Union[str, Any]							=   model_class.from_pretrained(a__						,    from_pt=a__       )

          snake_case :			List[Any]							=   fx_model_loaded(**a__       ).to_tuple()
          self.assertEqual(
              len(a__       )						,    len(a__       )						,    '''Output lengths differ between Flax and PyTorch'''       )
          for fx_output_loaded, pt_output in zip(a__						,    a__       ):
           self.assert_almost_equals(fx_output_loaded[:, -1]						,    pt_output[:, -1].numpy()						,    4e-2       )

       @is_pt_flax_cross_test
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case	,       snake_case :			List[Any]							=   self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__       ):
          # prepare inputs
          snake_case :			str							=   self._prepare_for_class(a__						,    a__       )
          snake_case :			List[Any]							=   {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

          # load corresponding PyTorch class
          snake_case :			Any							=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
          snake_case :			Union[str, Any]							=   getattr(a__						,    a__       )

          snake_case :			List[str]							=   pt_model_class(a__       ).eval()
          snake_case :			Tuple							=   False
          snake_case :			Optional[int]							=   model_class(a__						,    dtype=jnp.floataa       )

          snake_case :			Any							=   load_flax_weights_in_pytorch_model(a__						,    fx_model.params       )
          snake_case	,       snake_case :			Union[str, Any]							=   pt_inputs['''input_ids'''].shape
          snake_case :			List[Any]							=   np.random.randint(0						,    seq_length - 1						,    size=(batch_size,)       )
          for batch_idx, start_index in enumerate(a__       ):
           snake_case :			Union[str, Any]							=   0
           snake_case :			Tuple							=   1
           snake_case :			int							=   0
           snake_case :			Optional[int]							=   1

          # make sure weights are tied in PyTorch
          pt_model.tie_weights()

          with torch.no_grad():
           snake_case :			Optional[int]							=   pt_model(**a__       ).to_tuple()

          snake_case :			Any							=   fx_model(**a__       ).to_tuple()
          self.assertEqual(len(a__       )						,    len(a__       )						,    '''Output lengths differ between Flax and PyTorch'''       )
          for fx_output, pt_output in zip(a__						,    a__       ):
           self.assert_almost_equals(fx_output[:, -1]						,    pt_output[:, -1].numpy()						,    4e-2       )

          with tempfile.TemporaryDirectory() as tmpdirname:
           fx_model.save_pretrained(a__       )
           snake_case :			int							=   pt_model_class.from_pretrained(a__						,    from_flax=a__       )

          with torch.no_grad():
           snake_case :			int							=   pt_model_loaded(**a__       ).to_tuple()

          self.assertEqual(
              len(a__       )						,    len(a__       )						,    '''Output lengths differ between Flax and PyTorch'''       )
          for fx_output, pt_output in zip(a__						,    a__       ):
           self.assert_almost_equals(fx_output[:, -1]						,    pt_output[:, -1].numpy()						,    4e-2       )



       @slow
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
         snake_case :			str							=   model_class_name.from_pretrained('''facebook/xglm-564M'''       )
         snake_case :			List[Any]							=   model(np.ones((1, 1)       )       )
         self.assertIsNotNone(a__       )






