


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class      _lowercase					(				lowercase__):


       """simple docstring"""




       A__   		=     ["image_processor", "tokenizer"]
       A__   		=     "AutoImageProcessor"
       A__   		=     "AutoTokenizer"





       def __init__(  self				:     Tuple   , __lowerCamelCase				:     int=None   , __lowerCamelCase				:     Tuple=None   , **__lowerCamelCase				:     int			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[int]       =    None
             if "feature_extractor" in kwargs:
                   warnings.warn(
                       "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                       " instead."   , __lowerCamelCase   , )
                   lowerCamelCase__						:							Any       =    kwargs.pop("feature_extractor"			)

             lowerCamelCase__						:							Optional[int]       =    image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                   raise ValueError("You have to specify an image_processor."			)
             if tokenizer is None:
                   raise ValueError("You have to specify a tokenizer."			)

             super().__init__(__lowerCamelCase   , __lowerCamelCase			)
             lowerCamelCase__						:							Tuple       =    self.image_processor





       def __call__(  self				:     Dict   , __lowerCamelCase				:     Optional[int]=None   , __lowerCamelCase				:     Dict=None   , __lowerCamelCase				:     str=None   , **__lowerCamelCase				:     Dict			):


             '''simple docstring'''



             if text is None and images is None:
                   raise ValueError("You have to specify either text or images. Both cannot be none."			)

             if text is not None:
                   lowerCamelCase__						:							Optional[int]       =    self.tokenizer(__lowerCamelCase   , return_tensors=__lowerCamelCase   , **__lowerCamelCase			)

             if images is not None:
                   lowerCamelCase__						:							int       =    self.image_processor(__lowerCamelCase   , return_tensors=__lowerCamelCase   , **__lowerCamelCase			)

             if text is not None and images is not None:
                   lowerCamelCase__						:							Any       =    image_features.pixel_values
                   return encoding
             elif text is not None:
                   return encoding
             else:
                   return BatchEncoding(data=dict(**__lowerCamelCase			)   , tensor_type=__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Optional[int]   , *__lowerCamelCase				:     Optional[int]   , **__lowerCamelCase				:     Tuple			):


             '''simple docstring'''



             return self.tokenizer.batch_decode(*__lowerCamelCase   , **__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Optional[Any]   , *__lowerCamelCase				:     Tuple   , **__lowerCamelCase				:     Dict			):


             '''simple docstring'''



             return self.tokenizer.decode(*__lowerCamelCase   , **__lowerCamelCase			)





       @property
       def 		lowerCAmelCase	(  self				:     str			):


             '''simple docstring'''



             lowerCamelCase__						:							int       =    self.tokenizer.model_input_names
             lowerCamelCase__						:							str       =    self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)





       @property
       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             warnings.warn(
                 "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."   , __lowerCamelCase   , )
             return self.image_processor_class





       @property
       def 		lowerCAmelCase	(  self				:     Optional[int]			):


             '''simple docstring'''



             warnings.warn(
                 "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."   , __lowerCamelCase   , )
             return self.image_processor






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]			)


