'''simple docstring'''


import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
 from tests.trainer.test_trainer import (  # noqa
     RegressionModelConfig,
     RegressionPreTrainedModel,
     get_regression_trainer,
 )


set_seed(42)

snake_case__	:     Union[str, Any]      =    get_tests_dir('''fixtures''')
snake_case__	:     int      =    os.path.join(dirname(get_tests_dir()))
snake_case__	:     Union[str, Any]      =    dirname(os.path.abspath(__file__))

# default torch.distributed port
snake_case__	:     Dict      =    '''10999'''

snake_case__	:     str      =    '''t5-small'''

# *** Working Models ***
snake_case__	:     str      =    '''hf-internal-testing/tiny-albert'''
snake_case__	:     Optional[Any]      =    '''sshleifer/bart-tiny-random'''
snake_case__	:     Optional[Any]      =    '''hf-internal-testing/tiny-bert'''
snake_case__	:     Optional[int]      =    '''hf-internal-testing/tiny-random-bigbird_pegasus'''
snake_case__	:     List[Any]      =    '''hf-internal-testing/tiny-random-big_bird'''
snake_case__	:     List[Any]      =    '''hf-internal-testing/tiny-random-blenderbot'''
snake_case__	:     str      =    '''bigscience/bigscience-small-testing'''
snake_case__	:     Tuple      =    '''hf-internal-testing/tiny-random-deberta'''
snake_case__	:     List[str]      =    '''hf-internal-testing/tiny-random-deberta-v2'''
snake_case__	:     Any      =    '''sshleifer/tiny-distilbert-base-cased'''
snake_case__	:     Optional[Any]      =    '''hf-internal-testing/tiny-electra'''
snake_case__	:     Optional[int]      =    '''hf-internal-testing/tiny-random-flaubert'''
snake_case__	:     Union[str, Any]      =    '''stas/tiny-wmt19-en-de'''
snake_case__	:     Any      =    '''hf-internal-testing/tiny-random-funnel'''
snake_case__	:     Optional[Any]      =    '''sshleifer/tiny-gpt2'''
snake_case__	:     str      =    '''hf-internal-testing/tiny-random-gptj'''
snake_case__	:     List[Any]      =    '''hf-internal-testing/tiny-random-gpt_neo'''
snake_case__	:     Tuple      =    '''hf-internal-testing/tiny-layoutlm'''
snake_case__	:     List[Any]      =    '''hf-internal-testing/tiny-random-led'''
snake_case__	:     Tuple      =    '''hf-internal-testing/tiny-random-longformer'''
snake_case__	:     Union[str, Any]      =    '''stas/tiny-m2m_100'''  # hf tiny model is unsuitable
snake_case__	:     Dict      =    '''sshleifer/tiny-marian-en-de'''
snake_case__	:     Tuple      =    '''sshleifer/tiny-mbart'''
snake_case__	:     Optional[int]      =    '''hf-internal-testing/tiny-random-mobilebert'''
snake_case__	:     List[Any]      =    '''hf-internal-testing/tiny-random-mpnet'''
snake_case__	:     List[str]      =    '''stas/pegasus-cnn_dailymail-tiny-random'''
snake_case__	:     str      =    '''hf-internal-testing/tiny-random-prophetnet'''
snake_case__	:     List[str]      =    '''sshleifer/tiny-distilroberta-base'''
snake_case__	:     List[Any]      =    '''hf-internal-testing/tiny-random-squeezebert'''
snake_case__	:     str      =    '''patrickvonplaten/t5-tiny-random'''
snake_case__	:     Tuple      =    '''hf-internal-testing/tiny-random-t5-v1.1'''
snake_case__	:     str      =    '''hf-internal-testing/tiny-random-vit'''
snake_case__	:     int      =    '''hf-internal-testing/tiny-xlm-roberta'''
snake_case__	:     Union[str, Any]      =    '''sshleifer/tiny-xlnet-base-cased'''


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
snake_case__	:     Optional[Any]      =    '''hf-internal-testing/tiny-random-mt5'''
snake_case__	:     List[str]      =    '''hf-internal-testing/tiny-random-camembert'''
snake_case__	:     Optional[Any]      =    '''hf-internal-testing/tiny-random-openai-gpt'''

# missing tokenizer files
snake_case__	:     Optional[int]      =    '''hf-internal-testing/tiny-random-convbert'''
snake_case__	:     Optional[Any]      =    '''hf-internal-testing/tiny-random-layoutlmv2'''
snake_case__	:     Union[str, Any]      =    '''hf-internal-testing/tiny-random-hubert'''

# issues with tokenizer
snake_case__	:     Optional[Any]      =    '''hf-internal-testing/tiny-random-ctrl'''
snake_case__	:     Optional[int]      =    '''hf-internal-testing/tiny-random-transfo-xl'''  # same as ctrl

# other issues with tiny models
snake_case__	:     List[str]      =    '''hf-internal-testing/tiny-random-ibert'''  # multiple issues with either mlm/qa/clas
snake_case__	:     Dict      =    '''hf-internal-testing/tiny-random-reformer'''  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
snake_case__	:     Optional[Any]      =    '''hf-internal-testing/tiny-random-dpr'''
# - "dpr"  examples/research_projects/rag-end2end-retriever/
snake_case__	:     Optional[int]      =    '''hf-internal-testing/tiny-random-rag'''
# - "rag" research_projects
snake_case__	:     Tuple      =    ''''''
# - "luke" Entities classes - no plan to make such example
snake_case__	:     Dict      =    '''hf-internal-testing/tiny-random-lxmert'''
# - "lxmert" doesn't work with run_qa.py
snake_case__	:     Dict      =    '''hf-internal-testing/tiny-random-clip'''
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
snake_case__	:     Optional[Any]      =    '''hf-internal-testing/tiny-random-speech_to_text'''
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
snake_case__	:     Any      =    '''hf-internal-testing/tiny-random-tapas'''


def    _lowerCamelCase       ( lowerCamelCase_   :				int=False  ):



     """simple docstring"""





     UpperCAmelCase_				:  Optional[int]  = min(2      ,	get_gpu_count()  ) if distributed else 1
     UpperCAmelCase_				:  List[str]  = os.environ.get('DS_TEST_PORT'      ,	lowerCamelCase_  )
     return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()


def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Tuple  = F'''{FIXTURE_DIRECTORY}/tests_samples'''
     UpperCAmelCase_				:  List[Any]  = F'''{data_dir_samples}/wmt_en_ro'''
     UpperCAmelCase_				:  Optional[int]  = F'''{data_dir_samples}/xsum'''
     UpperCAmelCase_				:  Optional[int]  = '\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        '.split()

     # try to cover as many models as possible once (it's enough to run on one task per model)
     # but need a tiny model for each
     #
     # should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
     UpperCAmelCase_				:  Any  = {
         'trans': [
             'bart',
             'fsmt',
             'm2m_100',
             'marian',
             'mbart',
             't5',
             't5_v1',
             # "mt5", missing model files
         ],
         'sum': [
             'pegasus',
         ],
         'clm': [
             'big_bird',
             'bigbird_pegasus',
             'blenderbot',
             'bloom',
             'gpt2',
             'gpt_neo',
             'gptj',
             'xlm-roberta',
             'prophetnet',
             # "camembert", missing model files
         ],
         'mlm': [
             'albert',
             'deberta',
             'deberta-v2',
             'distilbert',
             'electra',
             'flaubert',
             'funnel',
             'layoutlm',
             # "reformer", # multiple issues with either mlm/qa/clas
         ],
         'qa': [
             'led',
             'longformer',
             'mobilebert',
             'mpnet',
             'roberta',
             'squeezebert',
             # "convbert", # missing tokenizer files
             # "layoutlmv2", missing model files
         ],
         'clas': [
             'bert',
             'xlnet',
             # "hubert", # missing tokenizer files
             # "ibert", # multiple issues with either mlm/qa/clas
             # "transfo-xl", # tokenizer issues as ctrl
             # "ctrl", # tokenizer issues
             # "openai-gpt", missing model files
             # "tapas", multiple issues
         ],
         'img_clas': [
             'vit',
         ],
     }

     UpperCAmelCase_				:  List[Any]  = F'''{ROOT_DIRECTORY}/examples/pytorch'''

     UpperCAmelCase_				:  Optional[int]  = {
         'trans': F'''
        {scripts_dir}/translation/run_translation.py
        --train_file {data_dir_wmt}/train.json
        --source_lang en
        --target_lang ro
        ''',
         'sum': F'''
        {scripts_dir}/summarization/run_summarization.py
        --train_file {data_dir_xsum}/sample.json
        --max_source_length 12
        --max_target_length 12
        --lang en
        ''',
         'clm': F'''
        {scripts_dir}/language-modeling/run_clm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        --block_size 8
        ''',
         'mlm': F'''
        {scripts_dir}/language-modeling/run_mlm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        ''',
         'qa': F'''
        {scripts_dir}/question-answering/run_qa.py
        --train_file {data_dir_samples}/SQUAD/sample.json
        ''',
         'clas': F'''
        {scripts_dir}/text-classification/run_glue.py
        --train_file {data_dir_samples}/MRPC/train.csv
        --max_seq_length 12
        --task_name MRPC
        ''',
         'img_clas': F'''
        {scripts_dir}/image-classification/run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --remove_unused_columns False
            --max_steps 10
            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
        ''',
     }

     UpperCAmelCase_				:  Union[str, Any]  = get_launcher(distributed=lowerCamelCase_  )

     UpperCAmelCase_				:  Optional[int]  = {}
     for task, args in tasks.items():
          UpperCAmelCase_				:  Optional[int]  = args.split()
          for model in tasksamodels[task]:
               UpperCAmelCase_				:  Any  = globals()[F'''{model.upper().replace('-'      ,	'_'  )}_TINY''']
               UpperCAmelCase_				:  Optional[int]  = F'''--model_name_or_path {model_name}'''.split()
               UpperCAmelCase_				:  Dict  = launcher + args + args_model + args_main

               # # generation special case
               # if task == "gen":
               #     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
               #     args_model += f"--model_type {model}".split()
               #     cmds[f"{task}_{model}"] = launcher + args + args_model
               # else:

     return cmds


snake_case__	:     Optional[int]      =    make_task_cmds()

snake_case__	:     str      =    '''zero2'''
snake_case__	:     str      =    '''zero3'''

snake_case__	:     Optional[int]      =    [ZEROa, ZEROa]


def    _lowerCamelCase       ( lowerCamelCase_   :				Optional[Any]      ,	lowerCamelCase_   :				Union[str, Any]      ,	lowerCamelCase_   :				List[str]  ):



     """simple docstring"""





     UpperCAmelCase_				:  List[str]  = parameterized.to_safe_name('_'.join(str(lowerCamelCase_  ) for x in param.args  )  )
     return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
snake_case__	:     Union[str, Any]      =    list(itertools.product(stages, task_cmds.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       if task not in task_cmds:
            raise ValueError(F'''don\'t know of task {task}, have {task_cmds.keys()}'''		)

       UpperCAmelCase_				:  Optional[int]  = task_cmds[task]
       UpperCAmelCase_				:  List[str]  = F'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

       UpperCAmelCase_				:  List[str]  = self.get_auto_remove_tmp_dir()
       UpperCAmelCase_				:  List[Any]  = F'''--output_dir {output_dir}'''.split()

       cmd += args_ds + args_out

       return cmd, output_dir






  @parameterized.expand(snake_case_ ,						name_func=snake_case_		)
  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Optional[int]  = self.get_task_cmd(snake_case_ ,						snake_case_		)

       # 1. generate the checkpoint
       cmd += "--save_steps 1".split()
       # keep for quick debug
       # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
       execute_subprocess_async(snake_case_ ,						env=self.get_env()		)

       # 2. test that the fp32 weights get reconsolidated
       UpperCAmelCase_				:  str  = F'''{output_dir}/checkpoint-1'''
       UpperCAmelCase_				:  Optional[Any]  = F'''{chkpt_dir}/out.bin'''
       UpperCAmelCase_				:  Union[str, Any]  = F'''{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'''
       # keep for quick debug
       # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
       subprocess.check_call(snake_case_ ,						shell=snake_case_		)
       assert os.path.exists(snake_case_		), F'''{recovered_model_path} was not found'''

       # possibly could also test that the resulting saved model is usable but given that we use
       # random models we won't know if it's any good












import unittest
from unittest import skip

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


if is_torch_available():
       import torch

       from transformers import JukeboxModel, JukeboxPrior, JukeboxTokenizer





@require_torch
class        lowerCamelCase__(   unittest.TestCase):
      UpperCAmelCase__ :      Optional[Any]       						=  (JukeboxModel,) if is_torch_available() else ()
      UpperCAmelCase__ :      Union[str, Any]       						=  'openai/jukebox-1b-lyrics'
      UpperCAmelCase__ :      Optional[int]       						=  {
          '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
      UpperCAmelCase__ :      Dict       						=  [
          1864, 1536, 1213, 1870, 1357, 1536, 519, 880, 1323, 789, 1082, 534,
          1000, 1445, 1105, 1130, 967, 515, 1434, 1620, 534, 1495, 283, 1445,
          333, 1307, 539, 1631, 1528, 375, 1434, 673, 627, 710, 778, 1883,
          1405, 1276, 1455, 1228
      ]

      UpperCAmelCase__ :      List[Any]       						=  [
          1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653
      ]

      UpperCAmelCase__ :      str       						=  [
          1125, 1751, 697, 1776, 1141, 1476, 391, 697, 1125, 684, 867, 416,
          844, 1372, 1274, 717, 1274, 844, 1299, 1419, 697, 1370, 317, 1125,
          191, 1440, 1370, 1440, 1370, 282, 1621, 1370, 368, 349, 867, 1872,
          1262, 869, 1728, 747
      ]
      UpperCAmelCase__ :      str       						=  [
          416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416
      ]

      UpperCAmelCase__ :      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
      ]
      UpperCAmelCase__ :      Tuple       						=  [
          854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842,
          185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842,
          591, 842, 1353, 842, 185, 842, 591, 842, 591, 114, 591, 842,
          185, 842, 591, 89
      ]

      UpperCAmelCase__ :      List[Any]       						=  [105_8304, 0, 78_6432, 7169, 507, 76, 27, 40, 30, 76]

      UpperCAmelCase__ :      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
      ]
      UpperCAmelCase__ :      List[str]       						=  [
          1236, 1668, 1484, 1920, 1848, 1409, 139, 864, 1828, 1272, 1599, 824,
          1672, 139, 555, 1484, 824, 1920, 555, 596, 1579, 1599, 1231, 1599,
          1637, 1407, 212, 824, 1599, 116, 1433, 824, 258, 1599, 1433, 1895,
          1063, 1433, 1433, 1599
      ]
      UpperCAmelCase__ :      str       						=  [
          1684, 1873, 1119, 1189, 395, 611, 1901, 972, 890, 1337, 1392, 1927,
          96, 972, 672, 780, 1119, 890, 158, 771, 1073, 1927, 353, 1331,
          1269, 1459, 1333, 1645, 812, 1577, 1337, 606, 353, 981, 1466, 619,
          197, 391, 302, 1930
      ]
      UpperCAmelCase__ :      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
      ]
      UpperCAmelCase__ :      Optional[int]       						=  [
          -0.04_92, -0.05_24, -0.05_65, -0.06_40, -0.06_86, -0.06_84, -0.06_77, -0.06_64,
          -0.06_05, -0.04_90, -0.03_30, -0.01_68, -0.00_83, -0.00_75, -0.00_51, 0.00_25,
          0.01_36, 0.02_61, 0.03_86, 0.04_97, 0.05_80, 0.05_99, 0.05_83, 0.06_14,
          0.07_40, 0.08_89, 0.10_23, 0.11_62, 0.12_11, 0.12_12, 0.12_51, 0.13_36,
          0.15_02, 0.16_86, 0.18_83, 0.21_48, 0.23_63, 0.24_58, 0.25_07, 0.25_31
      ]
      UpperCAmelCase__ :      Union[str, Any]       						=  [
          0.02_56, -0.05_44, 0.16_00, -0.00_32, 0.10_66, 0.08_25, -0.00_13, 0.34_40,
          0.02_10, 0.04_12, -0.17_77, -0.08_92, -0.01_64, 0.02_85, -0.06_13, -0.06_17,
          -0.01_37, -0.02_01, -0.01_75, 0.02_15, -0.06_27, 0.05_20, -0.07_30, 0.09_70,
          -0.01_00, 0.04_42, -0.05_86, 0.02_07, -0.00_15, -0.00_82
      ]
      UpperCAmelCase__ :      str       						=  [
          0.04_15, 0.08_77, 0.00_22, -0.00_55, 0.07_51, 0.03_34, 0.03_24, -0.00_68,
          0.00_11, 0.00_17, -0.06_76, 0.06_55, -0.01_43, 0.03_99, 0.03_03, 0.07_43,
          -0.01_68, -0.03_94, -0.11_13, 0.01_24, 0.04_42, 0.02_67, -0.00_03, -0.15_36,
          -0.01_16, -0.18_37, -0.01_80, -0.10_26, -0.07_77, -0.04_56
      ]
      UpperCAmelCase__ :      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:					Dict  ):
             __lowerCamelCase  =		JukeboxTokenizer.from_pretrained(self.model_id  )
             __lowerCamelCase  =		tokenizer(**self.metas  )["""input_ids"""]
             return tokens
      @slow
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             __lowerCamelCase  =		JukeboxModel.from_pretrained(self.model_id       ,			min_duration=0  ).eval()
             __lowerCamelCase  =		self.prepare_inputs()

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

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

             set_seed(0  )
             __lowerCamelCase  =		model._sample(UpperCamelCase_       ,			UpperCamelCase_       ,			[2]       ,			sample_length=40 * model.priors[2].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             self.assertIn(zs[2][0].detach().cpu().tolist()       ,			[self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]  )
      @slow
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             __lowerCamelCase  =		False
             __lowerCamelCase  =		JukeboxModel.from_pretrained(self.model_id       ,			min_duration=0  ).eval()

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

             __lowerCamelCase  =		model.priors[0]
             __lowerCamelCase  =		0
             __lowerCamelCase  =		top_prior.get_music_tokens_conds(UpperCamelCase_       ,			start=UpperCamelCase_       ,			end=start + top_prior.n_ctx  )
             __lowerCamelCase  =		top_prior.get_metadata(labels[0].clone()       ,			UpperCamelCase_       ,			1_05_83_04       ,			0  )

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

             __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase  =		top_prior.get_cond(UpperCamelCase_       ,			UpperCamelCase_  )
             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]  ):
             __lowerCamelCase  =		False

             __lowerCamelCase  =		JukeboxModel.from_pretrained(self.model_id       ,			min_duration=0  ).eval()
             set_seed(0  )
             __lowerCamelCase  =		torch.rand((1, 51_20, 1)  )
             __lowerCamelCase  =		list(self.prepare_inputs()  )

             __lowerCamelCase  =		[model.vqvae.encode(UpperCamelCase_       ,			start_level=2       ,			bs_chunks=waveform.shape[0]  )[0], None, None]
             __lowerCamelCase  =		model._sample(
                 UpperCamelCase_       ,			UpperCamelCase_       ,			sample_levels=[0]       ,			save_results=UpperCamelCase_       ,			sample_length=40 * model.priors[0].raw_to_tokens  )
             torch.testing.assert_allclose(zs[0][0][:40]       ,			torch.tensor(self.EXPECTED_PRIMED_0  )  )

             __lowerCamelCase  =		torch.cat((zs[0], torch.zeros(1       ,			20_48 - zs[0].shape[-1]  ))       ,			dim=-1  ).long()
             __lowerCamelCase  =		[upper_a, model.vqvae.encode(UpperCamelCase_       ,			start_level=1       ,			bs_chunks=waveform.shape[0]  )[0], None]
             __lowerCamelCase  =		model._sample(
                 UpperCamelCase_       ,			UpperCamelCase_       ,			sample_levels=[1]       ,			save_results=UpperCamelCase_       ,			sample_length=40 * model.priors[1].raw_to_tokens  )
             torch.testing.assert_allclose(zs[1][0][:40]       ,			torch.tensor(self.EXPECTED_PRIMED_1  )  )

             __lowerCamelCase  =		torch.cat((zs[1], torch.zeros(1       ,			20_48 - zs[1].shape[-1]  ))       ,			dim=-1  ).long()
             __lowerCamelCase  =		[upper_a, upper_a, model.vqvae.encode(UpperCamelCase_       ,			start_level=0       ,			bs_chunks=waveform.shape[0]  )[0]]
             __lowerCamelCase  =		model._sample(
                 UpperCamelCase_       ,			UpperCamelCase_       ,			sample_levels=[2]       ,			save_results=UpperCamelCase_       ,			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:					List[Any]  ):
             __lowerCamelCase  =		JukeboxModel.from_pretrained(self.model_id       ,			min_duration=0  ).eval()
             set_seed(0  )
             __lowerCamelCase  =		torch.rand((1, 51_20, 1)  )
             with torch.no_grad():
                    __lowerCamelCase  =		model.vqvae.encode(UpperCamelCase_       ,			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  =		model.vqvae.decode(UpperCamelCase_       ,			start_level=2       ,			bs_chunks=x.shape[0]  )
             torch.testing.assert_allclose(x[0, :40, 0]       ,			torch.tensor(self.EXPECTED_VQVAE_DECODE  )       ,			atol=1E-4       ,			rtol=1E-4  )





@require_torch
class        lowerCamelCase__(   unittest.TestCase):
      UpperCAmelCase__ :      List[Any]       						=  (JukeboxModel,) if is_torch_available() else ()
      UpperCAmelCase__ :      Optional[int]       						=  'openai/jukebox-5b-lyrics'
      UpperCAmelCase__ :      Optional[Any]       						=  {
          '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
      UpperCAmelCase__ :      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,
          1489, 1489, 1489, 1489, 1150, 1853, 1509, 1150, 1357, 1509, 6, 1272
      ]
      UpperCAmelCase__ :      Union[str, Any]       						=  [
          1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
      ]

      UpperCAmelCase__ :      Optional[int]       						=  [
          1125, 416, 1125, 1125, 1125, 1125, 1125, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
      ]
      UpperCAmelCase__ :      List[str]       						=  [
          416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416
      ]

      UpperCAmelCase__ :      Optional[Any]       						=  [
          1755, 1061, 234, 1755, 1061, 1755, 185, 290, 307, 307, 616, 616,
          616, 616, 616, 616, 307, 290, 417, 1755, 234, 1755, 185, 290,
          290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1755,
          234, 234, 1755, 234, 185, 185, 307, 616, 616, 616, 616, 290,
          1755, 1755, 1755, 234, 234, 1755, 1572, 290, 307, 616, 34, 616
      ]
      UpperCAmelCase__ :      str       						=  [
          854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842, 185,
          417, 185, 842, 307, 842, 591, 842, 185, 842, 185, 842, 591, 842,
          1353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591,
          89, 591, 842, 591, 842, 591, 417, 1372, 842, 1372, 842, 34, 842,
          185, 89, 591, 842, 185, 842, 591, 632
      ]

      UpperCAmelCase__ :      List[Any]       						=  [
          1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
      ]
      UpperCAmelCase__ :      Dict       						=  [
          1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 1853, 1177, 1536, 1228,
          710, 475, 1489, 1229, 1224, 231, 1224, 252, 1434, 653, 475,
          1106, 1877, 1599, 1228, 1600, 1683, 1182, 1853, 475, 1864,
          252, 1229, 1434, 2001
      ]

      UpperCAmelCase__ :      Optional[int]       						=  [
          1125, 1125, 416, 1125, 1125, 416, 1125, 1125, 416, 416, 1125, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
      ]
      UpperCAmelCase__ :      str       						=  [
          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:					Optional[Any]       ,			UpperCamelCase_:					Optional[int]  ):
             __lowerCamelCase  =		JukeboxTokenizer.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		tokenizer(**self.metas  )["""input_ids"""]
             return tokens
      @slow
      def      lowerCAmelCase__		(							self:					Tuple  ):
             __lowerCamelCase  =		JukeboxModel.from_pretrained(self.model_id       ,			min_duration=0  ).eval()
             __lowerCamelCase  =		self.prepare_inputs(self.model_id  )

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

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

             set_seed(0  )
             __lowerCamelCase  =		model._sample(UpperCamelCase_       ,			UpperCamelCase_       ,			[2]       ,			sample_length=60 * model.priors[2].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             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:					List[Any]  ):
             __lowerCamelCase  =		JukeboxModel.from_pretrained(self.model_id       ,			min_duration=0  ).eval()
             __lowerCamelCase  =		[i.cuda() for i in self.prepare_inputs(self.model_id  )]

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




      @slow
      def      lowerCAmelCase__		(							self:					Dict  ):
             __lowerCamelCase  =		"""ArthurZ/jukebox_prior_0"""
             __lowerCamelCase  =		JukeboxPrior.from_pretrained(UpperCamelCase_       ,			min_duration=0  ).eval().half().to("""cuda"""  )

             __lowerCamelCase  =		self.prepare_inputs(UpperCamelCase_  )[0].cuda()
             __lowerCamelCase  =		model.get_metadata(UpperCamelCase_       ,			0       ,			76_80       ,			0  )
             set_seed(0  )
             __lowerCamelCase  =		model.sample(1       ,			metadata=UpperCamelCase_       ,			sample_tokens=60  )
             self.assertIn(outputs[0].cpu().tolist()       ,			[self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]  )

