




'''simple docstring'''



import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
     import torch_xla.core.xla_model as xm  # noqa: F401

     UpperCAmelCase_       :  List[Any]      =		True
except ImportError:
     UpperCAmelCase_       :  Any      =		False


# Cache this result has it's a C FFI call which can be pretty time-consuming
UpperCAmelCase_       :  Optional[Any]      =		torch.distributed.is_available()







def 			_UpperCamelCase (_lowerCamelCase							:     Union[str, Any]	)->   Dict:




     '''simple docstring'''





     __snake_case    					=  importlib.util.find_spec(_lowerCamelCase	) is not None
     if package_exists:
          try:
               __snake_case    					=  importlib.metadata.metadata(_lowerCamelCase	)
               return True
          except importlib.metadata.PackageNotFoundError:
               return False







def 			_UpperCamelCase ()->   bool:




     '''simple docstring'''





     return _torch_distributed_available







def 			_UpperCamelCase ()->   List[Any]:




     '''simple docstring'''





     try:
          pass
     except ImportError:
          print(
              '''Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'''
              ''' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'''
              ''' Bindings for PyTorch*.'''	)
     return (
         importlib.util.find_spec('''torch_ccl'''	) is not None
         or importlib.util.find_spec('''oneccl_bindings_for_pytorch'''	) is not None
     )







def 			_UpperCamelCase ()->   Optional[Any]:




     '''simple docstring'''





     return importlib.metadata.version('''oneccl_bind_pt'''	)







def 			_UpperCamelCase ()->   Tuple:




     '''simple docstring'''





     return _is_package_available('''transformer_engine'''	)







@lru_cache
def 			_UpperCamelCase (_lowerCamelCase							:     Optional[int]=True	)->   Dict:




     '''simple docstring'''





     if torch.cuda.is_available():
          return False
     if _tpu_available and check_device:
          try:
               # Will raise a RuntimeError if no XLA configuration is found
               __snake_case    					=  xm.xla_device()
               return True
          except RuntimeError:
               return False
     return _tpu_available







def 			_UpperCamelCase ()->   Optional[int]:




     '''simple docstring'''





     return _is_package_available('''deepspeed'''	)







def 			_UpperCamelCase (_lowerCamelCase							:     Union[str, Any]=False	)->   Tuple:




     '''simple docstring'''





     if is_tpu_available():
          return not ignore_tpu
     if torch.cuda.is_available():
          return torch.cuda.is_bfaa_supported()
     if is_npu_available():
          return False
     return True







def 			_UpperCamelCase ()->   Optional[int]:




     '''simple docstring'''





     __snake_case    					=  _is_package_available('''bitsandbytes'''	)
     if package_exists:
          __snake_case    					=  version.parse(importlib.metadata.version('''bitsandbytes'''	)	)
          return compare_versions(_lowerCamelCase   ,	'''>='''   ,	'''0.39.0'''	)
     return False







def 			_UpperCamelCase ()->   Optional[int]:




     '''simple docstring'''





     __snake_case    					=  _is_package_available('''bitsandbytes'''	)
     if package_exists:
          __snake_case    					=  version.parse(importlib.metadata.version('''bitsandbytes'''	)	)
          return compare_versions(_lowerCamelCase   ,	'''>='''   ,	'''0.37.2'''	)
     return False







def 			_UpperCamelCase ()->   Optional[Any]:




     '''simple docstring'''





     return _is_package_available('''bitsandbytes'''	)







def 			_UpperCamelCase ()->   List[Any]:




     '''simple docstring'''





     if strtobool(os.environ.get('''ACCELERATE_USE_MEGATRON_LM'''   ,	'''False'''	)	) == 1:
          __snake_case    					=  importlib.util.find_spec('''megatron'''	) is not None
          if package_exists:
               try:
                    __snake_case    					=  parse(importlib.metadata.version('''megatron-lm'''	)	)
                    return compare_versions(_lowerCamelCase   ,	'''>='''   ,	'''2.2.0'''	)
               except Exception as e:
                    warnings.warn(f'''Parse Megatron version failed. Exception:{e}'''	)
                    return False







def 			_UpperCamelCase ()->   str:




     '''simple docstring'''





     return _is_package_available('''safetensors'''	)







def 			_UpperCamelCase ()->   List[str]:




     '''simple docstring'''





     return _is_package_available('''transformers'''	)







def 			_UpperCamelCase ()->   Dict:




     '''simple docstring'''





     return _is_package_available('''datasets'''	)







def 			_UpperCamelCase ()->   int:




     '''simple docstring'''





     return _is_package_available('''aim'''	)







def 			_UpperCamelCase ()->   List[str]:




     '''simple docstring'''





     return _is_package_available('''tensorboard'''	) or _is_package_available('''tensorboardX'''	)







def 			_UpperCamelCase ()->   Optional[int]:




     '''simple docstring'''





     return _is_package_available('''wandb'''	)







def 			_UpperCamelCase ()->   Optional[int]:




     '''simple docstring'''





     return _is_package_available('''comet_ml'''	)







def 			_UpperCamelCase ()->   Tuple:




     '''simple docstring'''





     return _is_package_available('''boto3'''	)







def 			_UpperCamelCase ()->   List[str]:




     '''simple docstring'''





     if _is_package_available('''rich'''	):
          if "ACCELERATE_DISABLE_RICH" in os.environ:
               warnings.warn(
                   '''`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.'''	)
               return not parse_flag_from_env('''ACCELERATE_DISABLE_RICH'''   ,	_lowerCamelCase	)
          return parse_flag_from_env('''ACCELERATE_ENABLE_RICH'''   ,	_lowerCamelCase	)
     return False







def 			_UpperCamelCase ()->   Optional[int]:




     '''simple docstring'''





     return _is_package_available('''sagemaker'''	)







def 			_UpperCamelCase ()->   str:




     '''simple docstring'''





     return _is_package_available('''tqdm'''	)







def 			_UpperCamelCase ()->   Union[str, Any]:




     '''simple docstring'''





     return _is_package_available('''mlflow'''	)







def 			_UpperCamelCase ()->   List[Any]:




     '''simple docstring'''





     return is_torch_version('''>='''   ,	'''1.12'''	) and torch.backends.mps.is_available() and torch.backends.mps.is_built()







def 			_UpperCamelCase ()->   Any:




     '''simple docstring'''





     def get_major_and_minor_from_version(_lowerCamelCase							:     str	):
          return str(version.parse(_lowerCamelCase	).major	) + "." + str(version.parse(_lowerCamelCase	).minor	)

     __snake_case    					=  importlib.metadata.version('''torch'''	)
     if importlib.util.find_spec('''intel_extension_for_pytorch'''	) is None:
          return False
     __snake_case    					=  '''N/A'''
     try:
          __snake_case    					=  importlib.metadata.version('''intel_extension_for_pytorch'''	)
     except importlib.metadata.PackageNotFoundError:
          return False
     __snake_case    					=  get_major_and_minor_from_version(_torch_version	)
     __snake_case    					=  get_major_and_minor_from_version(_ipex_version	)
     if torch_major_and_minor != ipex_major_and_minor:
          warnings.warn(
              f'''Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,'''
              f''' but PyTorch {_torch_version} is found. Please switch to the matching version and run again.'''	)
          return False
     return True







@lru_cache
def 			_UpperCamelCase (_lowerCamelCase							:     List[str]=False	)->   int:




     '''simple docstring'''





     if importlib.util.find_spec('''torch'''	) is None or importlib.util.find_spec('''torch_npu'''	) is None:
          return False

     import torch
     import torch_npu  # noqa: F401

     if check_device:
          try:
               # Will raise a RuntimeError if no NPU is found
               __snake_case    					=  torch.npu.device_count()
               return torch.npu.is_available()
          except RuntimeError:
               return False
     return hasattr(_lowerCamelCase   ,	'''npu'''	) and torch.npu.is_available()







@lru_cache
def 			_UpperCamelCase (_lowerCamelCase							:     Optional[int]=False	)->   List[Any]:




     '''simple docstring'''





     if not parse_flag_from_env('''ACCELERATE_USE_XPU'''   ,	default=_lowerCamelCase	):
          return False
     if is_ipex_available():
          import torch

          if is_torch_version('''<='''   ,	'''1.12'''	):
               return False
     else:
          return False

     import intel_extension_for_pytorch  # noqa: F401

     if check_device:
          try:
               # Will raise a RuntimeError if no XPU  is found
               __snake_case    					=  torch.xpu.device_count()
               return torch.xpu.is_available()
          except RuntimeError:
               return False
     return hasattr(_lowerCamelCase   ,	'''xpu'''	) and torch.xpu.is_available()












'''simple docstring'''



import unittest
from unittest import skip

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


if is_torch_available():
     import torch

     from transformers import JukeboxModel, JukeboxPrior, JukeboxTokenizer





@require_torch
class     lowerCAmelCase   ( unittest.TestCase):
       __lowercase  :      int        =			(JukeboxModel,) if is_torch_available() else ()
       __lowercase  :      Tuple        =			'''openai/jukebox-1b-lyrics'''
       __lowercase  :      Any        =			{
           '''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
       __lowercase  :      int        =			[
           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
       ]

       __lowercase  :      Optional[int]        =			[
           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
       ]

       __lowercase  :      Union[str, Any]        =			[
           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
       ]
       __lowercase  :      Tuple        =			[
           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
       ]

       __lowercase  :      str        =			[
           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
       ]
       __lowercase  :      Optional[int]        =			[
           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
       ]

       __lowercase  :      Dict        =			[105_8304, 0, 78_6432, 7169, 507, 76, 27, 40, 30, 76]

       __lowercase  :      Union[str, Any]        =			[
           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
       ]
       __lowercase  :      Tuple        =			[
           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
       ]
       __lowercase  :      Union[str, Any]        =			[
           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
       ]
       __lowercase  :      Union[str, Any]        =			[
           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
       ]
       __lowercase  :      List[Any]        =			[
           -0.0492, -0.0524, -0.0565, -0.0640, -0.0686, -0.0684, -0.0677, -0.0664,
           -0.0605, -0.0490, -0.0330, -0.0168, -0.0083, -0.0075, -0.0051, 0.0025,
           0.0136, 0.0261, 0.0386, 0.0497, 0.0580, 0.0599, 0.0583, 0.0614,
           0.0740, 0.0889, 0.1023, 0.1162, 0.1211, 0.1212, 0.1251, 0.1336,
           0.1502, 0.1686, 0.1883, 0.2148, 0.2363, 0.2458, 0.2507, 0.2531
       ]
       __lowercase  :      Optional[int]        =			[
           0.0256, -0.0544, 0.1600, -0.0032, 0.1066, 0.0825, -0.0013, 0.3440,
           0.0210, 0.0412, -0.1777, -0.0892, -0.0164, 0.0285, -0.0613, -0.0617,
           -0.0137, -0.0201, -0.0175, 0.0215, -0.0627, 0.0520, -0.0730, 0.0970,
           -0.0100, 0.0442, -0.0586, 0.0207, -0.0015, -0.0082
       ]
       __lowercase  :      List[Any]        =			[
           0.0415, 0.0877, 0.0022, -0.0055, 0.0751, 0.0334, 0.0324, -0.0068,
           0.0011, 0.0017, -0.0676, 0.0655, -0.0143, 0.0399, 0.0303, 0.0743,
           -0.0168, -0.0394, -0.1113, 0.0124, 0.0442, 0.0267, -0.0003, -0.1536,
           -0.0116, -0.1837, -0.0180, -0.1026, -0.0777, -0.0456
       ]
       __lowercase  :      Optional[int]        =			[
           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  )      ->  Union[str, Any]:



            '''simple docstring'''




            __snake_case    					=  JukeboxTokenizer.from_pretrained(self.model_id  )
            __snake_case    					=  tokenizer(**self.metas  )['''input_ids''']
            return tokens


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



            '''simple docstring'''




            __snake_case    					=  JukeboxModel.from_pretrained(self.model_id    ,  min_duration=0  ).eval()
            __snake_case    					=  self.prepare_inputs()

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

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

            set_seed(0  )
            __snake_case    					=  model._sample(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  [2]    ,  sample_length=40 * model.priors[2].raw_to_tokens    ,  save_results=__SCREAMING_SNAKE_CASE  )
            self.assertIn(zs[2][0].detach().cpu().tolist()    ,  [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]  )


       @slow
       def       lowerCAmelCase      (   self  )      ->  List[Any]:



            '''simple docstring'''




            __snake_case    					=  False
            __snake_case    					=  JukeboxModel.from_pretrained(self.model_id    ,  min_duration=0  ).eval()

            __snake_case    					=  self.prepare_inputs()
            set_seed(0  )
            __snake_case    					=  [torch.zeros(1    ,  0    ,  dtype=torch.long  ) for _ in range(3  )]

            __snake_case    					=  model.priors[0]
            __snake_case    					=  0
            __snake_case    					=  top_prior.get_music_tokens_conds(__SCREAMING_SNAKE_CASE    ,  start=__SCREAMING_SNAKE_CASE    ,  end=start + top_prior.n_ctx  )
            __snake_case    					=  top_prior.get_metadata(labels[0].clone()    ,  __SCREAMING_SNAKE_CASE    ,  105_8304    ,  0  )

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

            __snake_case					,    __snake_case					,    __snake_case    					=  top_prior.get_cond(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )
            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  )      ->  List[Any]:



            '''simple docstring'''




            __snake_case    					=  False

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

            __snake_case    					=  [model.vqvae.encode(__SCREAMING_SNAKE_CASE    ,  start_level=2    ,  bs_chunks=waveform.shape[0]  )[0], None, None]
            __snake_case    					=  model._sample(
                __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  sample_levels=[0]    ,  save_results=__SCREAMING_SNAKE_CASE    ,  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    					=  torch.cat((zs[0], torch.zeros(1    ,  2048 - zs[0].shape[-1]  ))    ,  dim=-1  ).long()
            __snake_case    					=  [upper_a, model.vqvae.encode(__SCREAMING_SNAKE_CASE    ,  start_level=1    ,  bs_chunks=waveform.shape[0]  )[0], None]
            __snake_case    					=  model._sample(
                __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  sample_levels=[1]    ,  save_results=__SCREAMING_SNAKE_CASE    ,  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    					=  torch.cat((zs[1], torch.zeros(1    ,  2048 - zs[1].shape[-1]  ))    ,  dim=-1  ).long()
            __snake_case    					=  [upper_a, upper_a, model.vqvae.encode(__SCREAMING_SNAKE_CASE    ,  start_level=0    ,  bs_chunks=waveform.shape[0]  )[0]]
            __snake_case    					=  model._sample(
                __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  sample_levels=[2]    ,  save_results=__SCREAMING_SNAKE_CASE    ,  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  )      ->  str:



            '''simple docstring'''




            __snake_case    					=  JukeboxModel.from_pretrained(self.model_id    ,  min_duration=0  ).eval()
            set_seed(0  )
            __snake_case    					=  torch.rand((1, 5120, 1)  )
            with torch.no_grad():
                 __snake_case    					=  model.vqvae.encode(__SCREAMING_SNAKE_CASE    ,  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    					=  model.vqvae.decode(__SCREAMING_SNAKE_CASE    ,  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):
       __lowercase  :      Tuple        =			(JukeboxModel,) if is_torch_available() else ()
       __lowercase  :      List[str]        =			'''openai/jukebox-5b-lyrics'''
       __lowercase  :      Any        =			{
           '''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
       __lowercase  :      List[str]        =			[
           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
       ]
       __lowercase  :      Optional[int]        =			[
           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
       ]

       __lowercase  :      Dict        =			[
           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
       ]
       __lowercase  :      Union[str, Any]        =			[
           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
       ]

       __lowercase  :      List[str]        =			[
           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
       ]
       __lowercase  :      Any        =			[
           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
       ]

       __lowercase  :      str        =			[
           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
       ]
       __lowercase  :      List[str]        =			[
           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
       ]

       __lowercase  :      List[str]        =			[
           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
       ]
       __lowercase  :      int        =			[
           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       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE  )      ->  str:



            '''simple docstring'''




            __snake_case    					=  JukeboxTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE  )
            __snake_case    					=  tokenizer(**self.metas  )['''input_ids''']
            return tokens


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



            '''simple docstring'''




            __snake_case    					=  JukeboxModel.from_pretrained(self.model_id    ,  min_duration=0  ).eval()
            __snake_case    					=  self.prepare_inputs(self.model_id  )

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

            set_seed(0  )
            __snake_case    					=  model._sample(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  [1]    ,  sample_length=60 * model.priors[1].raw_to_tokens    ,  save_results=__SCREAMING_SNAKE_CASE  )
            self.assertIn(zs[1][0].detach().cpu().tolist()    ,  [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]  )

            set_seed(0  )
            __snake_case    					=  model._sample(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  [2]    ,  sample_length=60 * model.priors[2].raw_to_tokens    ,  save_results=__SCREAMING_SNAKE_CASE  )
            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  )      ->  Any:



            '''simple docstring'''




            __snake_case    					=  JukeboxModel.from_pretrained(self.model_id    ,  min_duration=0  ).eval()
            __snake_case    					=  [i.cuda() for i in self.prepare_inputs(self.model_id  )]

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




       @slow
       def       lowerCAmelCase      (   self  )      ->  int:



            '''simple docstring'''




            __snake_case    					=  '''ArthurZ/jukebox_prior_0'''
            __snake_case    					=  JukeboxPrior.from_pretrained(__SCREAMING_SNAKE_CASE    ,  min_duration=0  ).eval().half().to('''cuda'''  )

            __snake_case    					=  self.prepare_inputs(__SCREAMING_SNAKE_CASE  )[0].cuda()
            __snake_case    					=  model.get_metadata(__SCREAMING_SNAKE_CASE    ,  0    ,  7680    ,  0  )
            set_seed(0  )
            __snake_case    					=  model.sample(1    ,  metadata=__SCREAMING_SNAKE_CASE    ,  sample_tokens=60  )
            self.assertIn(outputs[0].cpu().tolist()    ,  [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]  )






