




'''simple docstring'''



import argparse
import time

import torch
import transformers
from measures_util import end_measure, log_measures, start_measure
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer

from accelerate.utils import compute_module_sizes


UpperCAmelCase_       :  int      =		{
    '''gpt-j-6b''': {'''is_causal''': True, '''model''': '''sgugger/sharded-gpt-j-6B''', '''tokenizer''': '''EleutherAI/gpt-j-6B'''},
    '''gpt-neox''': {'''is_causal''': True, '''model''': '''EleutherAI/gpt-neox-20b'''},
    '''opt''': {'''is_causal''': True, '''model''': '''facebook/opt-30b'''},
    '''T0pp''': {'''is_causal''': False, '''model''': '''bigscience/T0pp''', '''model_revision''': '''sharded'''},
}

UpperCAmelCase_       :  Optional[int]      =		[
    '''Hello, my name is''',
    '''Are unicorns real? Unicorns are''',
    '''For the first time in several years,''',
    '''My name is Julien and I am''',
    '''The goal of life is''',
    '''Whenever I\'m sad, I like to''',
]







def 			_UpperCamelCase ()->   List[str]:




     '''simple docstring'''





     __snake_case    					=  argparse.ArgumentParser(description='''Run and time generations on a big model using Accelerate.'''	)
     parser.add_argument('''model_name'''   ,	type=_lowerCamelCase   ,	default=_lowerCamelCase   ,	help='''The name of the model to try.'''	)
     parser.add_argument(
         '''--tokenizer_name'''   ,	type=_lowerCamelCase   ,	default=_lowerCamelCase   ,	help='''The name of the tokenizer (if different from the model.'''	)
     parser.add_argument('''--is_causal'''   ,	type=_lowerCamelCase   ,	default=_lowerCamelCase   ,	help='''Whether or not the model is causal.'''	)
     parser.add_argument(
         '''--model_revision'''   ,	type=_lowerCamelCase   ,	default=_lowerCamelCase   ,	help='''The revision to use for the model checkpoint.'''	)
     parser.add_argument('''--torch_dtype'''   ,	type=_lowerCamelCase   ,	default=_lowerCamelCase   ,	help='''The dtype for the model.'''	)
     parser.add_argument('''--disk_offload'''   ,	action='''store_true'''	)

     __snake_case    					=  parser.parse_args()

     # Sanitize args
     if args.model_name in DEFAULT_MODELS:
          __snake_case    					=  DEFAULT_MODELS[args.model_name]
          __snake_case    					=  defaults['''model''']
          if args.tokenizer_name is None:
               __snake_case    					=  defaults.get('''tokenizer'''   ,	args.model_name	)
          if args.is_causal is None:
               __snake_case    					=  defaults['''is_causal''']
          if args.model_revision is None:
               __snake_case    					=  defaults.get('''model_revision'''   ,	'''main'''	)

     if args.is_causal is None:
          raise ValueError('''Could not infer the default for `--is_causal`, pass either True or False for it.'''	)
     if args.tokenizer_name is None:
          __snake_case    					=  args.model_name
     if args.model_revision is None:
          __snake_case    					=  '''main'''

     return args







def 			_UpperCamelCase ()->   Tuple:




     '''simple docstring'''





     transformers.utils.logging.set_verbosity_error()
     __snake_case    					=  parse_args()

     if args.torch_dtype is None:
          __snake_case    					=  AutoConfig.from_pretrained(args.model_name	)
          __snake_case    					=  getattr(_lowerCamelCase   ,	'''torch_dtype'''   ,	torch.floataa	)
     else:
          __snake_case    					=  getattr(_lowerCamelCase   ,	args.torch_dtype	)
     __snake_case    					=  AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
     __snake_case    					=  {
         '''torch_dtype''': torch_dtype,
         '''revision''': args.model_revision,
     }
     if args.disk_offload:
          __snake_case    					=  '''tmp_offload'''
          __snake_case    					=  True

     __snake_case    					=  start_measure()
     __snake_case    					=  model_cls.from_pretrained(args.model_name   ,	device_map='''auto'''   ,	**_lowerCamelCase	)
     __snake_case    					=  end_measure(_lowerCamelCase	)
     log_measures(_lowerCamelCase   ,	'''Model loading'''	)

     __snake_case    					=  compute_module_sizes(_lowerCamelCase	)
     __snake_case    					=  {v: 0 for v in model.hf_device_map.values()}
     for module, device in model.hf_device_map.items():
          device_size[device] += module_sizes[module]
     __snake_case    					=  '''\n'''.join([f'''- {device}: {size // 2**20}MiB''' for device, size in device_size.items()]	)
     print(f'''\nTheoretical use:\n{message}'''	)

     __snake_case    					=  AutoTokenizer.from_pretrained(args.tokenizer_name	)

     __snake_case    					=  start_measure()
     __snake_case    					=  []
     __snake_case    					=  []
     __snake_case    					=  []
     for prompt in PROMPTS:
          __snake_case    					=  tokenizer(_lowerCamelCase   ,	return_tensors='''pt'''	).to(0	)
          __snake_case    					=  inputs['''input_ids'''][0].tolist()
          __snake_case    					=  time.time()
          __snake_case    					=  model.generate(inputs['''input_ids''']	)
          __snake_case    					=  time.time()
          __snake_case    					=  outputs[0].tolist()
          __snake_case    					=  len(_lowerCamelCase	) if outputs[: len(_lowerCamelCase	)] != tokens else len(_lowerCamelCase	) - len(_lowerCamelCase	)
          __snake_case    					=  after_generate - before_generate

          __snake_case    					=  tokenizer.decode(_lowerCamelCase   ,	skip_special_tokens=_lowerCamelCase	)
          texts_outs.append(_lowerCamelCase	)
          generation_times.append(_lowerCamelCase	)
          gen_tokens.append(_lowerCamelCase	)
          print(f'''Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'''	)

     __snake_case    					=  end_measure(_lowerCamelCase	)
     log_measures(_lowerCamelCase   ,	'''Model generation'''	)

     __snake_case    					=  [gen / tok for gen, tok in zip(_lowerCamelCase   ,	_lowerCamelCase	)]
     __snake_case    					=  sum(_lowerCamelCase	) / len(_lowerCamelCase	)
     print(f'''Average time of generation per token: {avg_gen:.2f}s'''	)
     print(f'''First generation (avg time per token): {generation_times_per_token[0]:.2f}s'''	)
     __snake_case    					=  sum(generation_times_per_token[1:]	) / (len(_lowerCamelCase	) - 1)
     print(f'''Average time of generation per token (excluding the first): {avg_gen:.2f}s'''	)


if __name__ == "__main__":
     main()












'''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]  )






