





import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType



@require_torch
class 				_lowerCAmelCase			:


  '''simple docstring'''
  a_							:				Dict					    =()
  a_							:				List[Any]					    =True







  def UpperCamelCase_       (					self					:    Optional[int]							):


    '''simple docstring'''





    _snake_case      : Any														=		self.config_class

    # test default config
    _snake_case      : List[Any]														=		config_class()
    self.assertIsNotNone(UpperCamelCase							)
    _snake_case      : Optional[int]														=		['stem'] + [f"""stage{idx}""" for idx in range(1					, len(config.depths							) + 1							)]
    self.assertEqual(config.stage_names					, UpperCamelCase							)
    self.assertTrue(set(config.out_features							).issubset(set(config.stage_names							)							)							)

    # Test out_features and out_indices are correctly set
    # out_features and out_indices both None
    _snake_case      : List[str]														=		config_class(out_features=UpperCamelCase					, out_indices=UpperCamelCase							)
    self.assertEqual(config.out_features					, [config.stage_names[-1]]							)
    self.assertEqual(config.out_indices					, [len(config.stage_names							) - 1]							)

    # out_features and out_indices both set
    _snake_case      : List[str]														=		config_class(out_features=['stem', 'stage1']					, out_indices=[0, 1]							)
    self.assertEqual(config.out_features					, ['stem', 'stage1']							)
    self.assertEqual(config.out_indices					, [0, 1]							)

    # Only out_features set
    _snake_case      : Optional[int]														=		config_class(out_features=['stage1', 'stage3']							)
    self.assertEqual(config.out_features					, ['stage1', 'stage3']							)
    self.assertEqual(config.out_indices					, [1, 3]							)

    # Only out_indices set
    _snake_case      : Tuple														=		config_class(out_indices=[0, 2]							)
    self.assertEqual(config.out_features					, [config.stage_names[0], config.stage_names[2]]							)
    self.assertEqual(config.out_indices					, [0, 2]							)

    # Error raised when out_indices do not correspond to out_features
    with self.assertRaises(UpperCamelCase							):
      _snake_case      : Optional[Any]														=		config_class(out_features=['stage1', 'stage2']					, out_indices=[0, 2]							)







  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : List[str]														=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      _snake_case      : str														=		model_class(UpperCamelCase							)
      _snake_case      : Dict														=		inspect.signature(model.forward							)
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      _snake_case      : Optional[int]														=		[*signature.parameters.keys()]
      _snake_case      : Optional[Any]														=		['pixel_values']
      self.assertListEqual(arg_names[:1]					, UpperCamelCase							)







  def UpperCamelCase_       (					self					:    int							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : Dict														=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      _snake_case      : Dict														=		model_class(UpperCamelCase							)
      self.assertEqual(len(model.channels							)					, len(config.out_features							)							)
      _snake_case      : Tuple														=		model.num_features
      _snake_case      : str														=		[config.stage_names.index(UpperCamelCase							) for feat in config.out_features]
      _snake_case      : List[Any]														=		[num_features[idx] for idx in out_indices]
      self.assertListEqual(model.channels					, UpperCamelCase							)

      _snake_case      : List[str]														=		copy.deepcopy(UpperCamelCase							)
      _snake_case      : Dict														=		None
      _snake_case      : str														=		model_class(UpperCamelCase							)
      self.assertEqual(len(model.channels							)					, 1							)
      self.assertListEqual(model.channels					, [num_features[-1]]							)

      _snake_case      : Optional[Any]														=		copy.deepcopy(UpperCamelCase							)
      _snake_case      : List[Any]														=		None
      _snake_case      : Optional[int]														=		model_class(UpperCamelCase							)
      self.assertEqual(len(model.channels							)					, 1							)
      self.assertListEqual(model.channels					, [num_features[-1]]							)







  def UpperCamelCase_       (					self					:    Any							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : List[str]														=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      _snake_case      : str														=		model_class(UpperCamelCase							)
      model.to(UpperCamelCase							)
      model.eval()
      _snake_case      : List[str]														=		model(**UpperCamelCase							)

      self.assertEqual(len(result.feature_maps							)					, len(config.out_features							)							)
      self.assertEqual(len(model.channels							)					, len(config.out_features							)							)
      self.assertEqual(len(result.feature_maps							)					, len(config.out_indices							)							)
      self.assertEqual(len(model.channels							)					, len(config.out_indices							)							)

      # Check output of last stage is taken if out_features=None, out_indices=None
      _snake_case      : List[str]														=		copy.deepcopy(UpperCamelCase							)
      _snake_case      : Tuple														=		None
      _snake_case      : Union[str, Any]														=		model_class(UpperCamelCase							)
      model.to(UpperCamelCase							)
      model.eval()
      _snake_case      : Optional[Any]														=		model(**UpperCamelCase							)

      self.assertEqual(len(result.feature_maps							)					, 1							)
      self.assertEqual(len(model.channels							)					, 1							)

      _snake_case      : Optional[int]														=		copy.deepcopy(UpperCamelCase							)
      _snake_case      : Tuple														=		None
      _snake_case      : List[Any]														=		model_class(UpperCamelCase							)
      model.to(UpperCamelCase							)
      model.eval()
      _snake_case      : List[Any]														=		model(**UpperCamelCase							)

      self.assertEqual(len(result.feature_maps							)					, 1							)
      self.assertEqual(len(model.channels							)					, 1							)

      # Check backbone can be initialized with fresh weights
      _snake_case      : Dict														=		copy.deepcopy(UpperCamelCase							)
      _snake_case      : List[str]														=		False
      _snake_case      : Any														=		model_class(UpperCamelCase							)
      model.to(UpperCamelCase							)
      model.eval()
      _snake_case      : Tuple														=		model(**UpperCamelCase							)







  def UpperCamelCase_       (					self					:    int							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : Union[str, Any]														=		self.model_tester.prepare_config_and_inputs_for_common()

    for backbone_class in self.all_model_classes:
      _snake_case      : Optional[int]														=		backbone_class(UpperCamelCase							)

      self.assertTrue(hasattr(UpperCamelCase					, 'backbone_type'							)							)
      self.assertTrue(hasattr(UpperCamelCase					, 'stage_names'							)							)
      self.assertTrue(hasattr(UpperCamelCase					, 'num_features'							)							)
      self.assertTrue(hasattr(UpperCamelCase					, 'out_indices'							)							)
      self.assertTrue(hasattr(UpperCamelCase					, 'out_features'							)							)
      self.assertTrue(hasattr(UpperCamelCase					, 'out_feature_channels'							)							)
      self.assertTrue(hasattr(UpperCamelCase					, 'channels'							)							)

      self.assertIsInstance(backbone.backbone_type					, UpperCamelCase							)
      # Verify num_features has been initialized in the backbone init
      self.assertIsNotNone(backbone.num_features							)
      self.assertTrue(len(backbone.channels							) == len(backbone.out_indices							)							)
      self.assertTrue(len(backbone.stage_names							) == len(backbone.num_features							)							)
      self.assertTrue(len(backbone.channels							) <= len(backbone.num_features							)							)
      self.assertTrue(len(backbone.out_feature_channels							) == len(backbone.stage_names							)							)







  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : Union[str, Any]														=		self.model_tester.prepare_config_and_inputs_for_common()
    _snake_case      : List[Any]														=		inputs_dict['pixel_values'].shape[0]

    for backbone_class in self.all_model_classes:
      _snake_case      : List[str]														=		backbone_class(UpperCamelCase							)
      backbone.to(UpperCamelCase							)
      backbone.eval()

      _snake_case      : Union[str, Any]														=		backbone(**UpperCamelCase							)

      # Test default outputs and verify feature maps
      self.assertIsInstance(outputs.feature_maps					, UpperCamelCase							)
      self.assertTrue(len(outputs.feature_maps							) == len(backbone.channels							)							)
      for feature_map, n_channels in zip(outputs.feature_maps					, backbone.channels							):
        self.assertTrue(feature_map.shape[:2]					, (batch_size, n_channels)							)
      self.assertIsNone(outputs.hidden_states							)
      self.assertIsNone(outputs.attentions							)

      # Test output_hidden_states=True
      _snake_case      : str														=		backbone(**UpperCamelCase					, output_hidden_states=UpperCamelCase							)
      self.assertIsNotNone(outputs.hidden_states							)
      self.assertTrue(len(outputs.hidden_states							)					, len(backbone.stage_names							)							)
      for hidden_state, n_channels in zip(outputs.hidden_states					, backbone.channels							):
        self.assertTrue(hidden_state.shape[:2]					, (batch_size, n_channels)							)

      # Test output_attentions=True
      if self.has_attentions:
        _snake_case      : Tuple														=		backbone(**UpperCamelCase					, output_attentions=UpperCamelCase							)
        self.assertIsNotNone(outputs.attentions							)











"""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 	a				(					unittest.TestCase   ):
 snake_case__    	=    (JukeboxModel,) if is_torch_available() else ()
 snake_case__    	=    '''openai/jukebox-1b-lyrics'''
 snake_case__    	=    {
     '''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
 snake_case__    	=    [
     1_8_6_4, 1_5_3_6, 1_2_1_3, 1_8_7_0, 1_3_5_7, 1_5_3_6, 5_1_9, 8_8_0, 1_3_2_3, 7_8_9, 1_0_8_2, 5_3_4,
     1_0_0_0, 1_4_4_5, 1_1_0_5, 1_1_3_0, 9_6_7, 5_1_5, 1_4_3_4, 1_6_2_0, 5_3_4, 1_4_9_5, 2_8_3, 1_4_4_5,
     3_3_3, 1_3_0_7, 5_3_9, 1_6_3_1, 1_5_2_8, 3_7_5, 1_4_3_4, 6_7_3, 6_2_7, 7_1_0, 7_7_8, 1_8_8_3,
     1_4_0_5, 1_2_7_6, 1_4_5_5, 1_2_2_8
 ]

 snake_case__    	=    [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3
 ]

 snake_case__    	=    [
     1_1_2_5, 1_7_5_1, 6_9_7, 1_7_7_6, 1_1_4_1, 1_4_7_6, 3_9_1, 6_9_7, 1_1_2_5, 6_8_4, 8_6_7, 4_1_6,
     8_4_4, 1_3_7_2, 1_2_7_4, 7_1_7, 1_2_7_4, 8_4_4, 1_2_9_9, 1_4_1_9, 6_9_7, 1_3_7_0, 3_1_7, 1_1_2_5,
     1_9_1, 1_4_4_0, 1_3_7_0, 1_4_4_0, 1_3_7_0, 2_8_2, 1_6_2_1, 1_3_7_0, 3_6_8, 3_4_9, 8_6_7, 1_8_7_2,
     1_2_6_2, 8_6_9, 1_7_2_8, 7_4_7
 ]
 snake_case__    	=    [
     4_1_6, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6
 ]

 snake_case__    	=    [
     1_7_5_5, 8_4_2, 3_0_7, 1_8_4_3, 1_0_2_2, 1_3_9_5, 2_3_4, 1_5_5_4, 8_0_6, 7_3_9, 1_0_2_2, 4_4_2,
     6_1_6, 5_5_6, 2_6_8, 1_4_9_9, 9_3_3, 4_5_7, 1_4_4_0, 1_8_3_7, 7_5_5, 9_8_5, 3_0_8, 9_0_2,
     2_9_3, 1_4_4_3, 1_6_7_1, 1_1_4_1, 1_5_3_3, 5_5_5, 1_5_6_2, 1_0_6_1, 2_8_7, 4_1_7, 1_0_2_2, 2_0_0_8,
     1_1_8_6, 1_0_1_5, 1_7_7_7, 2_6_8
 ]
 snake_case__    	=    [
     8_5_4, 8_4_2, 1_3_5_3, 1_1_4, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 1_1_4, 5_9_1, 8_4_2,
     1_8_5, 4_1_7, 1_8_5, 8_4_2, 3_0_7, 8_4_2, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 3_0_7, 8_4_2,
     5_9_1, 8_4_2, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 1_1_4, 5_9_1, 8_4_2,
     1_8_5, 8_4_2, 5_9_1, 8_9
 ]

 snake_case__    	=    [1_0_5_8_3_0_4, 0, 7_8_6_4_3_2, 7_1_6_9, 5_0_7, 7_6, 2_7, 4_0, 3_0, 7_6]

 snake_case__    	=    [
     3_9_0, 1_1_6_0, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_9_0_7, 1_0_0_2, 1_0_0_2, 1_8_5_4, 1_0_0_2,
     1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_1_6_0, 1_1_6_0, 1_6_0_6, 5_9_6, 5_9_6, 1_1_6_0, 1_0_0_2,
     1_5_1_6, 5_9_6, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_8_5_4, 1_7_8_8, 1_9_0_7,
     1_9_0_7, 1_7_8_8, 5_9_6, 1_6_2_6
 ]
 snake_case__    	=    [
     1_2_3_6, 1_6_6_8, 1_4_8_4, 1_9_2_0, 1_8_4_8, 1_4_0_9, 1_3_9, 8_6_4, 1_8_2_8, 1_2_7_2, 1_5_9_9, 8_2_4,
     1_6_7_2, 1_3_9, 5_5_5, 1_4_8_4, 8_2_4, 1_9_2_0, 5_5_5, 5_9_6, 1_5_7_9, 1_5_9_9, 1_2_3_1, 1_5_9_9,
     1_6_3_7, 1_4_0_7, 2_1_2, 8_2_4, 1_5_9_9, 1_1_6, 1_4_3_3, 8_2_4, 2_5_8, 1_5_9_9, 1_4_3_3, 1_8_9_5,
     1_0_6_3, 1_4_3_3, 1_4_3_3, 1_5_9_9
 ]
 snake_case__    	=    [
     1_6_8_4, 1_8_7_3, 1_1_1_9, 1_1_8_9, 3_9_5, 6_1_1, 1_9_0_1, 9_7_2, 8_9_0, 1_3_3_7, 1_3_9_2, 1_9_2_7,
     9_6, 9_7_2, 6_7_2, 7_8_0, 1_1_1_9, 8_9_0, 1_5_8, 7_7_1, 1_0_7_3, 1_9_2_7, 3_5_3, 1_3_3_1,
     1_2_6_9, 1_4_5_9, 1_3_3_3, 1_6_4_5, 8_1_2, 1_5_7_7, 1_3_3_7, 6_0_6, 3_5_3, 9_8_1, 1_4_6_6, 6_1_9,
     1_9_7, 3_9_1, 3_0_2, 1_9_3_0
 ]
 snake_case__    	=    [
     3_9_0, 1_1_6_0, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_9_0_7, 1_0_0_2, 1_0_0_2, 1_8_5_4, 1_0_0_2,
     1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_1_6_0, 1_1_6_0, 1_6_0_6, 5_9_6, 5_9_6, 1_1_6_0, 1_0_0_2,
     1_5_1_6, 5_9_6, 1_0_0_2, 1_0_0_2, 1_0_0_2, 1_9_0_7, 1_7_8_8, 1_7_8_8, 1_7_8_8, 1_8_5_4, 1_7_8_8, 1_9_0_7,
     1_9_0_7, 1_7_8_8, 5_9_6, 1_6_2_6
 ]
 snake_case__    	=    [
     -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
 ]
 snake_case__    	=    [
     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
 ]
 snake_case__    	=    [
     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
 ]
 snake_case__    	=    [
     7_6, 2_7, 4_0, 3_0, 7_6, 4_6, 4_4, 4_7, 4_0, 3_7, 3_8, 3_1, 4_5, 4_5, 7_6, 3_8, 3_1, 3_3,
     4_5, 7_6, 4_1, 3_2, 7_6, 4_5, 4_6, 4_1, 4_0, 3_1, 7_8, 7_6
 ]



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  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(_snake_case							,    _snake_case							,    [0]							,    sample_length=40 * model.priors[0].raw_to_tokens							,    save_results=_snake_case			)
  self.assertIn(zs[0][0].detach().cpu().tolist()							,    [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]			)

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

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



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  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(_snake_case							,    start=_snake_case							,    end=start + top_prior.n_ctx			)
  lowerCAmelCase  							=      top_prior.get_metadata(labels[0].clone()							,    _snake_case							,    1_05_83_04							,    0			)

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

  lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase  							=      top_prior.get_cond(_snake_case							,    _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 					UpperCamelCase__   (			self			):







  """simple docstring"""






  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(_snake_case							,    start_level=2							,    bs_chunks=waveform.shape[0]			)[0], None, None]
  lowerCAmelCase  							=      model._sample(
      _snake_case							,    _snake_case							,    sample_levels=[0]							,    save_results=_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			)			)

  lowerCAmelCase  							=      torch.cat((zs[0], torch.zeros(1							,    20_48 - zs[0].shape[-1]			))							,    dim=-1			).long()
  lowerCAmelCase  							=      [upper_a, model.vqvae.encode(_snake_case							,    start_level=1							,    bs_chunks=waveform.shape[0]			)[0], None]
  lowerCAmelCase  							=      model._sample(
      _snake_case							,    _snake_case							,    sample_levels=[1]							,    save_results=_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			)			)

  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(_snake_case							,    start_level=0							,    bs_chunks=waveform.shape[0]			)[0]]
  lowerCAmelCase  							=      model._sample(
      _snake_case							,    _snake_case							,    sample_levels=[2]							,    save_results=_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 					UpperCamelCase__   (			self			):







  """simple docstring"""






  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(_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():
   lowerCAmelCase  							=      model.vqvae.decode(_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 	a				(					unittest.TestCase   ):
 snake_case__    	=    (JukeboxModel,) if is_torch_available() else ()
 snake_case__    	=    '''openai/jukebox-5b-lyrics'''
 snake_case__    	=    {
     '''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
 snake_case__    	=    [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     1_4_8_9, 1_4_8_9, 1_4_8_9, 1_4_8_9, 1_1_5_0, 1_8_5_3, 1_5_0_9, 1_1_5_0, 1_3_5_7, 1_5_0_9, 6, 1_2_7_2
 ]
 snake_case__    	=    [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3
 ]

 snake_case__    	=    [
     1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 1_1_2_5, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
 ]
 snake_case__    	=    [
     4_1_6, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
 ]

 snake_case__    	=    [
     1_7_5_5, 1_0_6_1, 2_3_4, 1_7_5_5, 1_0_6_1, 1_7_5_5, 1_8_5, 2_9_0, 3_0_7, 3_0_7, 6_1_6, 6_1_6,
     6_1_6, 6_1_6, 6_1_6, 6_1_6, 3_0_7, 2_9_0, 4_1_7, 1_7_5_5, 2_3_4, 1_7_5_5, 1_8_5, 2_9_0,
     2_9_0, 2_9_0, 3_0_7, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 2_9_0, 2_3_4, 2_3_4, 1_7_5_5,
     2_3_4, 2_3_4, 1_7_5_5, 2_3_4, 1_8_5, 1_8_5, 3_0_7, 6_1_6, 6_1_6, 6_1_6, 6_1_6, 2_9_0,
     1_7_5_5, 1_7_5_5, 1_7_5_5, 2_3_4, 2_3_4, 1_7_5_5, 1_5_7_2, 2_9_0, 3_0_7, 6_1_6, 3_4, 6_1_6
 ]
 snake_case__    	=    [
     8_5_4, 8_4_2, 1_3_5_3, 1_1_4, 1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 1_1_4, 5_9_1, 8_4_2, 1_8_5,
     4_1_7, 1_8_5, 8_4_2, 3_0_7, 8_4_2, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2,
     1_3_5_3, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 1_1_4, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 5_9_1,
     8_9, 5_9_1, 8_4_2, 5_9_1, 8_4_2, 5_9_1, 4_1_7, 1_3_7_2, 8_4_2, 1_3_7_2, 8_4_2, 3_4, 8_4_2,
     1_8_5, 8_9, 5_9_1, 8_4_2, 1_8_5, 8_4_2, 5_9_1, 6_3_2
 ]

 snake_case__    	=    [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3
 ]
 snake_case__    	=    [
     1_4_8_9, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3,
     6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 6_5_3, 1_8_5_3, 1_1_7_7, 1_5_3_6, 1_2_2_8,
     7_1_0, 4_7_5, 1_4_8_9, 1_2_2_9, 1_2_2_4, 2_3_1, 1_2_2_4, 2_5_2, 1_4_3_4, 6_5_3, 4_7_5,
     1_1_0_6, 1_8_7_7, 1_5_9_9, 1_2_2_8, 1_6_0_0, 1_6_8_3, 1_1_8_2, 1_8_5_3, 4_7_5, 1_8_6_4,
     2_5_2, 1_2_2_9, 1_4_3_4, 2_0_0_1
 ]

 snake_case__    	=    [
     1_1_2_5, 1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 1_1_2_5, 1_1_2_5, 4_1_6, 4_1_6, 1_1_2_5, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6,
     4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6, 4_1_6
 ]
 snake_case__    	=    [
     4_9_1, 1_7_5_5, 3_4, 1_6_1_3, 1_7_5_5, 4_1_7, 9_9_2, 1_6_1_3, 2_2_2, 8_4_2, 1_3_5_3, 1_6_1_3,
     8_4_4, 6_3_2, 1_8_5, 1_6_1_3, 8_4_4, 6_3_2, 1_8_5, 1_6_1_3, 1_8_5, 8_4_2, 6_7_7, 1_6_1_3,
     1_8_5, 1_1_4, 1_3_5_3, 1_6_1_3, 3_0_7, 8_9, 8_4_4, 1_6_1_3, 3_0_7, 1_3_3_2, 2_3_4, 1_9_7_9,
     3_0_7, 8_9, 1_3_5_3, 6_1_6, 3_4, 8_4_2, 1_8_5, 8_4_2, 3_4, 8_4_2, 1_8_5, 8_4_2,
     3_0_7, 1_1_4, 1_8_5, 8_9, 3_4, 1_2_6_8, 1_8_5, 8_9, 3_4, 8_4_2, 1_8_5, 8_9
 ]



 def 					UpperCamelCase__   (			self							,    _snake_case			):







  """simple docstring"""






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



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  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(_snake_case							,    _snake_case							,    [0]							,    sample_length=60 * model.priors[0].raw_to_tokens							,    save_results=_snake_case			)
  self.assertIn(zs[0][0].detach().cpu().tolist()							,    [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]			)

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

  set_seed(0			)
  lowerCAmelCase  							=      model._sample(_snake_case							,    _snake_case							,    [2]							,    sample_length=60 * model.priors[2].raw_to_tokens							,    save_results=_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 					UpperCamelCase__   (			self			):







  """simple docstring"""






  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(_snake_case							,    _snake_case							,    [0]							,    sample_length=60 * model.priors[0].raw_to_tokens							,    save_results=_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()
  lowerCAmelCase  							=      model._sample(_snake_case							,    _snake_case							,    [1]							,    sample_length=60 * model.priors[1].raw_to_tokens							,    save_results=_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()
  lowerCAmelCase  							=      model._sample(_snake_case							,    _snake_case							,    [2]							,    sample_length=60 * model.priors[2].raw_to_tokens							,    save_results=_snake_case			)
  torch.testing.assert_allclose(zs[2][0].cpu()							,    torch.tensor(self.EXPECTED_GPU_OUTPUTS_0			)			)



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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

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





