


import unittest
from unittest import skip

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow
from transformers.trainer_utils import set_seed


if is_torch_available():
    import torch

    from transformers import JukeboxModel, JukeboxPrior, JukeboxTokenizer







@require_torch
class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       A__   		=     (JukeboxModel,) if is_torch_available() else ()
       A__   		=     "openai/jukebox-1b-lyrics"
       A__   		=     {
           "artist": "Zac Brown Band",
           "genres": "Country",
           "lyrics": "I met a traveller from an antique land,\n    Who said \"Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ",
       }
       # fmt: off
       A__   		=     [
           18_64, 15_36, 12_13, 18_70, 13_57, 15_36, 5_19, 8_80, 13_23, 7_89, 10_82, 5_34,
           10_00, 14_45, 11_05, 11_30, 9_67, 5_15, 14_34, 16_20, 5_34, 14_95, 2_83, 14_45,
           3_33, 13_07, 5_39, 16_31, 15_28, 3_75, 14_34, 6_73, 6_27, 7_10, 7_78, 18_83,
           14_05, 12_76, 14_55, 12_28
       ]

       A__   		=     [
           14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53
       ]

       A__   		=     [
           11_25, 17_51, 6_97, 17_76, 11_41, 14_76, 3_91, 6_97, 11_25, 6_84, 8_67, 4_16,
           8_44, 13_72, 12_74, 7_17, 12_74, 8_44, 12_99, 14_19, 6_97, 13_70, 3_17, 11_25,
           1_91, 14_40, 13_70, 14_40, 13_70, 2_82, 16_21, 13_70, 3_68, 3_49, 8_67, 18_72,
           12_62, 8_69, 17_28, 7_47
       ]
       A__   		=     [
           4_16, 4_16, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16
       ]

       A__   		=     [
           17_55, 8_42, 3_07, 18_43, 10_22, 13_95, 2_34, 15_54, 8_06, 7_39, 10_22, 4_42,
           6_16, 5_56, 2_68, 14_99, 9_33, 4_57, 14_40, 18_37, 7_55, 9_85, 3_08, 9_02,
           2_93, 14_43, 16_71, 11_41, 15_33, 5_55, 15_62, 10_61, 2_87, 4_17, 10_22, 20_08,
           11_86, 10_15, 17_77, 2_68
       ]
       A__   		=     [
           8_54, 8_42, 13_53, 1_14, 13_53, 8_42, 1_85, 8_42, 1_85, 1_14, 5_91, 8_42,
           1_85, 4_17, 1_85, 8_42, 3_07, 8_42, 5_91, 8_42, 1_85, 8_42, 3_07, 8_42,
           5_91, 8_42, 13_53, 8_42, 1_85, 8_42, 5_91, 8_42, 5_91, 1_14, 5_91, 8_42,
           1_85, 8_42, 5_91, 89
       ]

       A__   		=     [1_05_83_04, 0, 78_64_32, 71_69, 5_07, 76, 27, 40, 30, 76]

       A__   		=     [
           3_90, 11_60, 10_02, 19_07, 17_88, 17_88, 17_88, 19_07, 10_02, 10_02, 18_54, 10_02,
           10_02, 10_02, 10_02, 10_02, 10_02, 11_60, 11_60, 16_06, 5_96, 5_96, 11_60, 10_02,
           15_16, 5_96, 10_02, 10_02, 10_02, 19_07, 17_88, 17_88, 17_88, 18_54, 17_88, 19_07,
           19_07, 17_88, 5_96, 16_26
       ]
       A__   		=     [
           12_36, 16_68, 14_84, 19_20, 18_48, 14_09, 1_39, 8_64, 18_28, 12_72, 15_99, 8_24,
           16_72, 1_39, 5_55, 14_84, 8_24, 19_20, 5_55, 5_96, 15_79, 15_99, 12_31, 15_99,
           16_37, 14_07, 2_12, 8_24, 15_99, 1_16, 14_33, 8_24, 2_58, 15_99, 14_33, 18_95,
           10_63, 14_33, 14_33, 15_99
       ]
       A__   		=     [
           16_84, 18_73, 11_19, 11_89, 3_95, 6_11, 19_01, 9_72, 8_90, 13_37, 13_92, 19_27,
           96, 9_72, 6_72, 7_80, 11_19, 8_90, 1_58, 7_71, 10_73, 19_27, 3_53, 13_31,
           12_69, 14_59, 13_33, 16_45, 8_12, 15_77, 13_37, 6_06, 3_53, 9_81, 14_66, 6_19,
           1_97, 3_91, 3_02, 19_30
       ]
       A__   		=     [
           3_90, 11_60, 10_02, 19_07, 17_88, 17_88, 17_88, 19_07, 10_02, 10_02, 18_54, 10_02,
           10_02, 10_02, 10_02, 10_02, 10_02, 11_60, 11_60, 16_06, 5_96, 5_96, 11_60, 10_02,
           15_16, 5_96, 10_02, 10_02, 10_02, 19_07, 17_88, 17_88, 17_88, 18_54, 17_88, 19_07,
           19_07, 17_88, 5_96, 16_26
       ]
       A__   		=     [
           -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
       ]
       A__   		=     [
           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
       ]
       A__   		=     [
           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
       ]
       A__   		=     [
           76, 27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45, 45, 76, 38, 31, 33,
           45, 76, 41, 32, 76, 45, 46, 41, 40, 31, 78, 76
       ]





       def 		lowerCAmelCase	(  self				:     Optional[int]			):


             '''simple docstring'''



             lowerCamelCase__						:							Tuple       =    JukeboxTokenizer.from_pretrained(self.model_id			)
             lowerCamelCase__						:							Dict       =    tokenizer(**self.metas			)["input_ids"]
             return tokens





       @slow
       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[int]       =    JukeboxModel.from_pretrained(self.model_id   , min_duration=0			).eval()
             lowerCamelCase__						:							Optional[Any]       =    self.prepare_inputs()

             set_seed(0			)
             lowerCamelCase__						:							List[str]       =    [torch.zeros(1   , 0   , dtype=torch.long			).cpu() for _ in range(3			)]
             lowerCamelCase__						:							Union[str, Any]       =    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__						:							Optional[Any]       =    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__						:							List[str]       =    model._sample(__lowerCamelCase   , __lowerCamelCase   , [2]   , sample_length=40 * model.priors[2].raw_to_tokens   , save_results=__lowerCamelCase			)
             self.assertIn(zs[2][0].detach().cpu().tolist()   , [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]			)





       @slow
       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							str       =    False
             lowerCamelCase__						:							Any       =    JukeboxModel.from_pretrained(self.model_id   , min_duration=0			).eval()

             lowerCamelCase__						:							Optional[Any]       =    self.prepare_inputs()
             set_seed(0			)
             lowerCamelCase__						:							List[Any]       =    [torch.zeros(1   , 0   , dtype=torch.long			) for _ in range(3			)]

             lowerCamelCase__						:							Optional[int]       =    model.priors[0]
             lowerCamelCase__						:							List[str]       =    0
             lowerCamelCase__						:							List[str]       =    top_prior.get_music_tokens_conds(__lowerCamelCase   , start=__lowerCamelCase   , end=start + top_prior.n_ctx			)
             lowerCamelCase__						:							List[Any]       =    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__						:							str       =    top_prior.get_cond(__lowerCamelCase   , __lowerCamelCase			)
             torch.testing.assert_allclose(
                 audio_conditioning[0][0][:30].detach()   , torch.tensor(self.EXPECTED_AUDIO_COND			)   , atol=1E-4   , rtol=1E-4			)
             torch.testing.assert_allclose(
                 metadata_conditioning[0][0][:30].detach()   , torch.tensor(self.EXPECTED_META_COND			)   , atol=1E-4   , rtol=1E-4			)
             torch.testing.assert_allclose(
                 lyric_tokens[0, :30].detach()   , torch.tensor(self.EXPECTED_LYRIC_COND			)   , atol=1E-4   , rtol=1E-4			)





       @slow
       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Dict       =    False

             lowerCamelCase__						:							List[Any]       =    JukeboxModel.from_pretrained(self.model_id   , min_duration=0			).eval()
             set_seed(0			)
             lowerCamelCase__						:							int       =    torch.rand((1, 5120, 1)			)
             lowerCamelCase__						:							int       =    list(self.prepare_inputs()			)

             lowerCamelCase__						:							List[str]       =    [model.vqvae.encode(__lowerCamelCase   , start_level=2   , bs_chunks=waveform.shape[0]			)[0], None, None]
             lowerCamelCase__						:							str       =    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__						:							Tuple       =    torch.cat((zs[0], torch.zeros(1   , 2048 - zs[0].shape[-1]			))   , dim=-1			).long()
             lowerCamelCase__						:							Dict       =    [upper_a, model.vqvae.encode(__lowerCamelCase   , start_level=1   , bs_chunks=waveform.shape[0]			)[0], None]
             lowerCamelCase__						:							str       =    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__						:							List[Any]       =    torch.cat((zs[1], torch.zeros(1   , 2048 - zs[1].shape[-1]			))   , dim=-1			).long()
             lowerCamelCase__						:							Union[str, Any]       =    [upper_a, upper_a, model.vqvae.encode(__lowerCamelCase   , start_level=0   , bs_chunks=waveform.shape[0]			)[0]]
             lowerCamelCase__						:							str       =    model._sample(
                 __lowerCamelCase   , __lowerCamelCase   , sample_levels=[2]   , save_results=__lowerCamelCase   , sample_length=40 * model.priors[2].raw_to_tokens			)
             torch.testing.assert_allclose(zs[2][0][:40].cpu()   , torch.tensor(self.EXPECTED_PRIMED_2			)			)





       @slow
       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							int       =    JukeboxModel.from_pretrained(self.model_id   , min_duration=0			).eval()
             set_seed(0			)
             lowerCamelCase__						:							Dict       =    torch.rand((1, 5120, 1)			)
             with torch.no_grad():
                   lowerCamelCase__						:							Optional[int]       =    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__						:							Tuple       =    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      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       A__   		=     (JukeboxModel,) if is_torch_available() else ()
       A__   		=     "openai/jukebox-5b-lyrics"
       A__   		=     {
           "artist": "Zac Brown Band",
           "genres": "Country",
           "lyrics": "I met a traveller from an antique land,\n    Who said \"Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ",
       }

       # fmt: off
       A__   		=     [
           14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           14_89, 14_89, 14_89, 14_89, 11_50, 18_53, 15_09, 11_50, 13_57, 15_09, 6, 12_72
       ]
       A__   		=     [
           14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53
       ]

       A__   		=     [
           11_25, 4_16, 11_25, 11_25, 11_25, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
       ]
       A__   		=     [
           4_16, 4_16, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
       ]

       A__   		=     [
           17_55, 10_61, 2_34, 17_55, 10_61, 17_55, 1_85, 2_90, 3_07, 3_07, 6_16, 6_16,
           6_16, 6_16, 6_16, 6_16, 3_07, 2_90, 4_17, 17_55, 2_34, 17_55, 1_85, 2_90,
           2_90, 2_90, 3_07, 6_16, 6_16, 6_16, 6_16, 6_16, 2_90, 2_34, 2_34, 17_55,
           2_34, 2_34, 17_55, 2_34, 1_85, 1_85, 3_07, 6_16, 6_16, 6_16, 6_16, 2_90,
           17_55, 17_55, 17_55, 2_34, 2_34, 17_55, 15_72, 2_90, 3_07, 6_16, 34, 6_16
       ]
       A__   		=     [
           8_54, 8_42, 13_53, 1_14, 13_53, 8_42, 1_85, 8_42, 1_85, 1_14, 5_91, 8_42, 1_85,
           4_17, 1_85, 8_42, 3_07, 8_42, 5_91, 8_42, 1_85, 8_42, 1_85, 8_42, 5_91, 8_42,
           13_53, 8_42, 1_85, 8_42, 5_91, 8_42, 5_91, 1_14, 5_91, 8_42, 1_85, 8_42, 5_91,
           89, 5_91, 8_42, 5_91, 8_42, 5_91, 4_17, 13_72, 8_42, 13_72, 8_42, 34, 8_42,
           1_85, 89, 5_91, 8_42, 1_85, 8_42, 5_91, 6_32
       ]

       A__   		=     [
           14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53
       ]
       A__   		=     [
           14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 18_53, 11_77, 15_36, 12_28,
           7_10, 4_75, 14_89, 12_29, 12_24, 2_31, 12_24, 2_52, 14_34, 6_53, 4_75,
           11_06, 18_77, 15_99, 12_28, 16_00, 16_83, 11_82, 18_53, 4_75, 18_64,
           2_52, 12_29, 14_34, 20_01
       ]

       A__   		=     [
           11_25, 11_25, 4_16, 11_25, 11_25, 4_16, 11_25, 11_25, 4_16, 4_16, 11_25, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
       ]
       A__   		=     [
           4_91, 17_55, 34, 16_13, 17_55, 4_17, 9_92, 16_13, 2_22, 8_42, 13_53, 16_13,
           8_44, 6_32, 1_85, 16_13, 8_44, 6_32, 1_85, 16_13, 1_85, 8_42, 6_77, 16_13,
           1_85, 1_14, 13_53, 16_13, 3_07, 89, 8_44, 16_13, 3_07, 13_32, 2_34, 19_79,
           3_07, 89, 13_53, 6_16, 34, 8_42, 1_85, 8_42, 34, 8_42, 1_85, 8_42,
           3_07, 1_14, 1_85, 89, 34, 12_68, 1_85, 89, 34, 8_42, 1_85, 89
       ]





       def 		lowerCAmelCase	(  self				:     Tuple   , __lowerCamelCase				:     List[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							str       =    JukeboxTokenizer.from_pretrained(__lowerCamelCase			)
             lowerCamelCase__						:							List[Any]       =    tokenizer(**self.metas			)["input_ids"]
             return tokens





       @slow
       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



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

             set_seed(0			)
             lowerCamelCase__						:							int       =    [torch.zeros(1   , 0   , dtype=torch.long			).cpu() for _ in range(3			)]
             lowerCamelCase__						:							Union[str, Any]       =    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__						:							List[Any]       =    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__						:							Tuple       =    model._sample(__lowerCamelCase   , __lowerCamelCase   , [2]   , sample_length=60 * model.priors[2].raw_to_tokens   , save_results=__lowerCamelCase			)
             self.assertIn(zs[2][0].detach().cpu().tolist()   , [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]			)





       @slow
       @skip("Not enough GPU memory on CI runners"			)
       def 		lowerCAmelCase	(  self				:     Union[str, Any]			):


             '''simple docstring'''



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

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





       @slow
       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             lowerCamelCase__						:							Tuple       =    "ArthurZ/jukebox_prior_0"
             lowerCamelCase__						:							Optional[Any]       =    JukeboxPrior.from_pretrained(__lowerCamelCase   , min_duration=0			).eval().half().to("cuda"			)

             lowerCamelCase__						:							Dict       =    self.prepare_inputs(__lowerCamelCase			)[0].cuda()
             lowerCamelCase__						:							Union[str, Any]       =    model.get_metadata(__lowerCamelCase   , 0   , 7680   , 0			)
             set_seed(0			)
             lowerCamelCase__						:							Optional[int]       =    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]			)






import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MarkupLMForQuestionAnswering,
        MarkupLMForSequenceClassification,
        MarkupLMForTokenClassification,
        MarkupLMModel,
    )

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer







class      _lowercase					:


       """simple docstring"""




       def __init__(  self				:     Dict   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     Union[str, Any]=13   , __lowerCamelCase				:     Optional[Any]=7   , __lowerCamelCase				:     List[str]=True   , __lowerCamelCase				:     str=True   , __lowerCamelCase				:     List[str]=True   , __lowerCamelCase				:     List[str]=True   , __lowerCamelCase				:     Optional[int]=99   , __lowerCamelCase				:     Dict=32   , __lowerCamelCase				:     List[str]=5   , __lowerCamelCase				:     str=4   , __lowerCamelCase				:     str=37   , __lowerCamelCase				:     Any="gelu"   , __lowerCamelCase				:     Dict=0.1   , __lowerCamelCase				:     Optional[Any]=0.1   , __lowerCamelCase				:     List[str]=512   , __lowerCamelCase				:     str=16   , __lowerCamelCase				:     List[Any]=2   , __lowerCamelCase				:     int=0.0_2   , __lowerCamelCase				:     Any=3   , __lowerCamelCase				:     Optional[int]=None   , __lowerCamelCase				:     str=20   , __lowerCamelCase				:     Union[str, Any]=30   , __lowerCamelCase				:     str=2   , __lowerCamelCase				:     Dict=2   , __lowerCamelCase				:     str=10   , ):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[int]       =    parent
             lowerCamelCase__						:							Optional[Any]       =    batch_size
             lowerCamelCase__						:							Union[str, Any]       =    seq_length
             lowerCamelCase__						:							Union[str, Any]       =    is_training
             lowerCamelCase__						:							Tuple       =    use_input_mask
             lowerCamelCase__						:							Tuple       =    use_token_type_ids
             lowerCamelCase__						:							Union[str, Any]       =    use_labels
             lowerCamelCase__						:							str       =    vocab_size
             lowerCamelCase__						:							int       =    hidden_size
             lowerCamelCase__						:							List[Any]       =    num_hidden_layers
             lowerCamelCase__						:							List[Any]       =    num_attention_heads
             lowerCamelCase__						:							Optional[Any]       =    intermediate_size
             lowerCamelCase__						:							int       =    hidden_act
             lowerCamelCase__						:							Optional[Any]       =    hidden_dropout_prob
             lowerCamelCase__						:							Optional[Any]       =    attention_probs_dropout_prob
             lowerCamelCase__						:							Optional[int]       =    max_position_embeddings
             lowerCamelCase__						:							Optional[Any]       =    type_vocab_size
             lowerCamelCase__						:							Tuple       =    type_sequence_label_size
             lowerCamelCase__						:							List[Any]       =    initializer_range
             lowerCamelCase__						:							str       =    num_labels
             lowerCamelCase__						:							Dict       =    scope
             lowerCamelCase__						:							List[Any]       =    max_xpath_tag_unit_embeddings
             lowerCamelCase__						:							str       =    max_xpath_subs_unit_embeddings
             lowerCamelCase__						:							Optional[Any]       =    tag_pad_id
             lowerCamelCase__						:							Optional[Any]       =    subs_pad_id
             lowerCamelCase__						:							Optional[int]       =    max_depth





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             lowerCamelCase__						:							Dict       =    ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size			)

             lowerCamelCase__						:							List[Any]       =    ids_tensor(
                 [self.batch_size, self.seq_length, self.max_depth]   , self.max_xpath_tag_unit_embeddings			)

             lowerCamelCase__						:							int       =    ids_tensor(
                 [self.batch_size, self.seq_length, self.max_depth]   , self.max_xpath_subs_unit_embeddings			)

             lowerCamelCase__						:							List[Any]       =    None
             if self.use_input_mask:
                   lowerCamelCase__						:							Tuple       =    ids_tensor([self.batch_size, self.seq_length]   , vocab_size=2			)

             lowerCamelCase__						:							int       =    None
             if self.use_token_type_ids:
                   lowerCamelCase__						:							Union[str, Any]       =    ids_tensor([self.batch_size, self.seq_length]   , self.type_vocab_size			)

             lowerCamelCase__						:							Any       =    None
             lowerCamelCase__						:							str       =    None
             if self.use_labels:
                   lowerCamelCase__						:							Dict       =    ids_tensor([self.batch_size]   , self.type_sequence_label_size			)
                   lowerCamelCase__						:							Optional[Any]       =    ids_tensor([self.batch_size, self.seq_length]   , self.num_labels			)

             lowerCamelCase__						:							int       =    self.get_config()

             return (
                 config,
                 input_ids,
                 xpath_tags_seq,
                 xpath_subs_seq,
                 token_type_ids,
                 input_mask,
                 sequence_labels,
                 token_labels,
             )





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             return MarkupLMConfig(
                 vocab_size=self.vocab_size   , hidden_size=self.hidden_size   , num_hidden_layers=self.num_hidden_layers   , num_attention_heads=self.num_attention_heads   , intermediate_size=self.intermediate_size   , hidden_act=self.hidden_act   , hidden_dropout_prob=self.hidden_dropout_prob   , attention_probs_dropout_prob=self.attention_probs_dropout_prob   , max_position_embeddings=self.max_position_embeddings   , type_vocab_size=self.type_vocab_size   , initializer_range=self.initializer_range   , max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings   , max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings   , tag_pad_id=self.tag_pad_id   , subs_pad_id=self.subs_pad_id   , max_depth=self.max_depth   , )





       def 		lowerCAmelCase	(  self				:     Optional[Any]   , __lowerCamelCase				:     Union[str, Any]   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     str   , __lowerCamelCase				:     Tuple   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     str   , ):


             '''simple docstring'''



             lowerCamelCase__						:							List[Any]       =    MarkupLMModel(config=__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             print("Configs:"   , model.config.tag_pad_id   , model.config.subs_pad_id			)
             lowerCamelCase__						:							Optional[int]       =    model(__lowerCamelCase   , attention_mask=__lowerCamelCase   , token_type_ids=__lowerCamelCase			)
             lowerCamelCase__						:							List[str]       =    model(__lowerCamelCase   , token_type_ids=__lowerCamelCase			)
             lowerCamelCase__						:							str       =    model(__lowerCamelCase			)
             self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, self.seq_length, self.hidden_size)			)
             self.parent.assertEqual(result.pooler_output.shape   , (self.batch_size, self.hidden_size)			)





       def 		lowerCAmelCase	(  self				:     Dict   , __lowerCamelCase				:     Union[str, Any]   , __lowerCamelCase				:     Tuple   , __lowerCamelCase				:     int   , __lowerCamelCase				:     Dict   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     Tuple   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     List[Any]   , ):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[Any]       =    self.num_labels
             lowerCamelCase__						:							str       =    MarkupLMForSequenceClassification(__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             lowerCamelCase__						:							Optional[int]       =    model(
                 __lowerCamelCase   , xpath_tags_seq=__lowerCamelCase   , xpath_subs_seq=__lowerCamelCase   , attention_mask=__lowerCamelCase   , token_type_ids=__lowerCamelCase   , labels=__lowerCamelCase   , )
             self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)			)





       def 		lowerCAmelCase	(  self				:     Any   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     int   , __lowerCamelCase				:     str   , ):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    self.num_labels
             lowerCamelCase__						:							Tuple       =    MarkupLMForTokenClassification(config=__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             lowerCamelCase__						:							List[Any]       =    model(
                 __lowerCamelCase   , xpath_tags_seq=__lowerCamelCase   , xpath_subs_seq=__lowerCamelCase   , attention_mask=__lowerCamelCase   , token_type_ids=__lowerCamelCase   , labels=__lowerCamelCase   , )
             self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.num_labels)			)





       def 		lowerCAmelCase	(  self				:     Tuple   , __lowerCamelCase				:     int   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     Dict   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     Any   , ):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    MarkupLMForQuestionAnswering(config=__lowerCamelCase			)
             model.to(__lowerCamelCase			)
             model.eval()
             lowerCamelCase__						:							Union[str, Any]       =    model(
                 __lowerCamelCase   , xpath_tags_seq=__lowerCamelCase   , xpath_subs_seq=__lowerCamelCase   , attention_mask=__lowerCamelCase   , token_type_ids=__lowerCamelCase   , start_positions=__lowerCamelCase   , end_positions=__lowerCamelCase   , )
             self.parent.assertEqual(result.start_logits.shape   , (self.batch_size, self.seq_length)			)
             self.parent.assertEqual(result.end_logits.shape   , (self.batch_size, self.seq_length)			)





       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    self.prepare_config_and_inputs()
             (
                 (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  (
                 lowerCamelCase__
             )       ,  
             )						:							Optional[int]       =    config_and_inputs
             lowerCamelCase__						:							List[str]       =    {
                 "input_ids": input_ids,
                 "xpath_tags_seq": xpath_tags_seq,
                 "xpath_subs_seq": xpath_subs_seq,
                 "token_type_ids": token_type_ids,
                 "attention_mask": input_mask,
             }
             return config, inputs_dict







@require_torch
class      _lowercase					(				lowercase__				,    lowercase__				,    unittest.TestCase):


       """simple docstring"""




       A__   		=     (
           (
               MarkupLMModel,
               MarkupLMForSequenceClassification,
               MarkupLMForTokenClassification,
               MarkupLMForQuestionAnswering,
           )
           if is_torch_available()
           else None
       )
       A__   		=     (
           {
               "feature-extraction": MarkupLMModel,
               "question-answering": MarkupLMForQuestionAnswering,
               "text-classification": MarkupLMForSequenceClassification,
               "token-classification": MarkupLMForTokenClassification,
               "zero-shot": MarkupLMForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )





       def 		lowerCAmelCase	(  self				:     int   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     int   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     List[str]			):


             '''simple docstring'''



             return True





       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    MarkupLMModelTester(self			)
             lowerCamelCase__						:							Any       =    ConfigTester(self   , config_class=__lowerCamelCase   , hidden_size=37			)





       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             lowerCamelCase__						:							Dict       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[Any]       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							List[Any]       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase			)



def  lowercase_						(						):

      """simple docstring"""




      lowerCamelCase__						:							int       =    "\n    <!DOCTYPE html>\n    <html>\n    <head>\n    <title>Page Title</title>\n    </head>\n    <body>\n\n    <h1>This is a Heading</h1>\n    <p>This is a paragraph.</p>\n\n    </body>\n    </html>\n    "

      return html_string







@require_torch
class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       @cached_property
       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    MarkupLMFeatureExtractor()
             lowerCamelCase__						:							Union[str, Any]       =    MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base"			)

             return MarkupLMProcessor(__lowerCamelCase   , __lowerCamelCase			)





       @slow
       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    MarkupLMModel.from_pretrained("microsoft/markuplm-base"			).to(__lowerCamelCase			)

             lowerCamelCase__						:							Dict       =    self.default_processor

             lowerCamelCase__						:							Dict       =    processor(prepare_html_string()   , return_tensors="pt"			)
             lowerCamelCase__						:							Optional[Any]       =    inputs.to(__lowerCamelCase			)

             # forward pass
             with torch.no_grad():
                   lowerCamelCase__						:							Union[str, Any]       =    model(**__lowerCamelCase			)

             # verify the last hidden states
             lowerCamelCase__						:							Dict       =    torch.Size([1, 14, 768]			)
             self.assertEqual(outputs.last_hidden_state.shape   , __lowerCamelCase			)

             lowerCamelCase__						:							Dict       =    torch.tensor(
                 [[0.0_6_7_5, -0.0_0_5_2, 0.5_0_0_1], [-0.2_2_8_1, 0.0_8_0_2, 0.2_1_9_2], [-0.0_5_8_3, -0.3_3_1_1, 0.1_1_8_5]]			).to(__lowerCamelCase			)

             self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]   , __lowerCamelCase   , atol=1E-4			)			)


