




'''simple docstring'''



import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowerCamelCase_		       =						logging.get_logger(__name__)

lowerCamelCase_		       =						{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''mask_emb''': '''masked_spec_embed''',
}
def  SCREAMING_SNAKE_CASE_ (							__A				: Tuple       ,     __A				: Any       ,     __A				: Any       ,     __A				: List[Any]       ,     __A				: int  ) -> Tuple:
   for attribute in key.split("."  ):
      _SCREAMING_SNAKE_CASE       =      getattr(_UpperCAmelCase       ,     _UpperCAmelCase  )

   if weight_type is not None:
      _SCREAMING_SNAKE_CASE       =      getattr(_UpperCAmelCase       ,     _UpperCAmelCase  ).shape
   else:
      _SCREAMING_SNAKE_CASE       =      hf_pointer.shape

   assert hf_shape == value.shape, (
       f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
       f""" {value.shape} for {full_name}"""
   )

   if weight_type == "weight":
      _SCREAMING_SNAKE_CASE       =      value
   elif weight_type == "weight_g":
      _SCREAMING_SNAKE_CASE       =      value
   elif weight_type == "weight_v":
      _SCREAMING_SNAKE_CASE       =      value
   elif weight_type == "bias":
      _SCREAMING_SNAKE_CASE       =      value
   else:
      _SCREAMING_SNAKE_CASE       =      value

   logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""  )
def  SCREAMING_SNAKE_CASE_ (							__A				: Optional[int]       ,     __A				: int  ) -> Tuple:
   _SCREAMING_SNAKE_CASE       =      []
   _SCREAMING_SNAKE_CASE       =      fairseq_model.state_dict()

   _SCREAMING_SNAKE_CASE       =      hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      _SCREAMING_SNAKE_CASE       =      False
      if "conv_layers" in name:
         load_conv_layer(
             _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     hf_model.config.feat_extract_norm == "group"       ,     )
         _SCREAMING_SNAKE_CASE       =      True
      else:
         for key, mapped_key in MAPPING.items():
            _SCREAMING_SNAKE_CASE       =      mapped_key

            if key in name:
               _SCREAMING_SNAKE_CASE       =      True
               if "*" in mapped_key:
                  _SCREAMING_SNAKE_CASE       =      name.split(_UpperCAmelCase  )[0].split("."  )[-2]
                  _SCREAMING_SNAKE_CASE       =      mapped_key.replace("*"       ,     _UpperCAmelCase  )
               if "weight_g" in name:
                  _SCREAMING_SNAKE_CASE       =      "weight_g"
               elif "weight_v" in name:
                  _SCREAMING_SNAKE_CASE       =      "weight_v"
               elif "weight" in name:
                  _SCREAMING_SNAKE_CASE       =      "weight"
               elif "bias" in name:
                  _SCREAMING_SNAKE_CASE       =      "bias"
               else:
                  _SCREAMING_SNAKE_CASE       =      None
               set_recursively(_UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase  )
            continue
      if not is_used:
         unused_weights.append(_UpperCAmelCase  )

   logger.warning(f"""Unused weights: {unused_weights}"""  )
def  SCREAMING_SNAKE_CASE_ (							__A				: Dict       ,     __A				: List[Any]       ,     __A				: Tuple       ,     __A				: Dict       ,     __A				: Optional[Any]  ) -> Optional[int]:
   _SCREAMING_SNAKE_CASE       =      full_name.split("conv_layers."  )[-1]
   _SCREAMING_SNAKE_CASE       =      name.split("."  )
   _SCREAMING_SNAKE_CASE       =      int(items[0]  )
   _SCREAMING_SNAKE_CASE       =      int(items[1]  )

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
         )
         _SCREAMING_SNAKE_CASE       =      value
         logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""  )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
         )
         _SCREAMING_SNAKE_CASE       =      value
         logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""  )
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
             " found."
         )
         _SCREAMING_SNAKE_CASE       =      value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
         )
         _SCREAMING_SNAKE_CASE       =      value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
   else:
      unused_weights.append(_UpperCAmelCase  )
def  SCREAMING_SNAKE_CASE_ (							__A				: int  ) -> Optional[int]:
   _SCREAMING_SNAKE_CASE       =      HubertConfig()
   _SCREAMING_SNAKE_CASE       =      model.config

   _SCREAMING_SNAKE_CASE       =      fs_config.activation_dropout
   _SCREAMING_SNAKE_CASE       =      False
   _SCREAMING_SNAKE_CASE       =      fs_config.attention_dropout
   _SCREAMING_SNAKE_CASE       =      False
   _SCREAMING_SNAKE_CASE       =      eval(fs_config.extractor_conv_feature_layers  )
   _SCREAMING_SNAKE_CASE       =      [x[0] for x in conv_layers]
   _SCREAMING_SNAKE_CASE       =      [x[1] for x in conv_layers]
   _SCREAMING_SNAKE_CASE       =      [x[2] for x in conv_layers]
   _SCREAMING_SNAKE_CASE       =      "gelu"
   _SCREAMING_SNAKE_CASE       =      "layer" if fs_config.extractor_mode == "layer_norm" else "group"
   _SCREAMING_SNAKE_CASE       =      False
   _SCREAMING_SNAKE_CASE       =      0.0
   _SCREAMING_SNAKE_CASE       =      0.0
   _SCREAMING_SNAKE_CASE       =      fs_config.activation_fn
   _SCREAMING_SNAKE_CASE       =      fs_config.dropout
   _SCREAMING_SNAKE_CASE       =      fs_config.encoder_embed_dim
   _SCREAMING_SNAKE_CASE       =      0.0_2
   _SCREAMING_SNAKE_CASE       =      fs_config.encoder_ffn_embed_dim
   _SCREAMING_SNAKE_CASE       =      1e-5
   _SCREAMING_SNAKE_CASE       =      0.0
   _SCREAMING_SNAKE_CASE       =      fs_config.encoder_attention_heads
   _SCREAMING_SNAKE_CASE       =      fs_config.conv_pos_groups
   _SCREAMING_SNAKE_CASE       =      fs_config.conv_pos
   _SCREAMING_SNAKE_CASE       =      len(_UpperCAmelCase  )
   _SCREAMING_SNAKE_CASE       =      fs_config.encoder_layers

   return config
@torch.no_grad()
def  SCREAMING_SNAKE_CASE_ (							__A				: str       ,     __A				: Union[str, Any]=None  ) -> List[Any]:
   _SCREAMING_SNAKE_CASE       =      distilhubert().model.model

   if config_path is not None:
      _SCREAMING_SNAKE_CASE       =      HubertConfig.from_pretrained(_UpperCAmelCase  )
   else:
      _SCREAMING_SNAKE_CASE       =      convert_config(_UpperCAmelCase  )
   _SCREAMING_SNAKE_CASE       =      model.eval()

   _SCREAMING_SNAKE_CASE       =      WavaVecaFeatureExtractor(
       feature_size=1       ,     sampling_rate=1_60_00       ,     padding_value=0       ,     do_normalize=_UpperCAmelCase       ,     return_attention_mask=_UpperCAmelCase       ,     )
   _SCREAMING_SNAKE_CASE       =      HubertModel(_UpperCAmelCase  )

   recursively_load_weights(_UpperCAmelCase       ,     _UpperCAmelCase  )

   feature_extractor.save_pretrained(_UpperCAmelCase  )
   hf_model.save_pretrained(_UpperCAmelCase  )


if __name__ == "__main__":
 lowerCamelCase_		       =						argparse.ArgumentParser()
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
 lowerCamelCase_		       =						parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)








"""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]			)





