





"""simple docstring"""
import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('''SDP_ENABLED''') or is_sagemaker_dp_enabled():
       __UpperCamelCase			:		Optional[Any]		    =   True
       __UpperCamelCase			:		Optional[int]		    =   '''p3dn.24xlarge'''
       import smdistributed.dataparallel.tensorflow as sdp
else:
       __UpperCamelCase			:		int		    =   False

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  Any   ,			_UpperCAmelCase :  Optional[Any]   ,			_UpperCAmelCase :  int   ,			_UpperCAmelCase :  Union[str, Any]   ,			_UpperCAmelCase :  Optional[int]   ,			_UpperCAmelCase :  Dict   ,			_UpperCAmelCase :  Optional[int]=None       ):
 lowerCAmelCase  							=      tqdm(_UpperCAmelCase       )
 for i, batch in enumerate(_UpperCAmelCase       ):
  with tf.GradientTape() as tape:
   lowerCAmelCase							,lowerCAmelCase  							=      batch
   lowerCAmelCase  							=      model(_UpperCAmelCase       )
   lowerCAmelCase  							=      loss(_UpperCAmelCase   ,			outputs.logits       )

  if SDP_ENABLED:
   lowerCAmelCase  							=      sdp.DistributedGradientTape(_UpperCAmelCase   ,			sparse_as_dense=_UpperCAmelCase       )

  lowerCAmelCase  							=      tape.gradient(_UpperCAmelCase   ,			model.trainable_variables       )
  opt.apply_gradients(zip(_UpperCAmelCase   ,			model.trainable_variables       )       )

  pbar.set_description(F'Loss: {loss_value:.4f}'       )

  if SDP_ENABLED and i == 0:
   sdp.broadcast_variables(model.variables   ,			root_rank=0       )
   sdp.broadcast_variables(opt.variables()   ,			root_rank=0       )

  if max_steps and i >= max_steps:
   break

 lowerCAmelCase  							=      {'loss': loss_value.numpy()}
 return train_results

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  List[Any]   ,			_UpperCAmelCase :  Union[str, Any]   ,			_UpperCAmelCase :  List[str]       ):
 # Load dataset
 lowerCAmelCase							,lowerCAmelCase  							=      load_dataset('imdb'   ,			split=['train', 'test']       )

 # Preprocess train dataset
 lowerCAmelCase  							=      train_dataset.map(
     lambda _UpperCAmelCase       : tokenizer(e['text']   ,			truncation=_UpperCAmelCase   ,			padding='max_length'       )   ,			batched=_UpperCAmelCase       )
 train_dataset.set_format(type='tensorflow'   ,			columns=['input_ids', 'attention_mask', 'label']       )

 lowerCAmelCase  							=      {
     x: train_dataset[x].to_tensor(default_value=0   ,			shape=[None, tokenizer.model_max_length]       )
     for x in ['input_ids', 'attention_mask']
 }
 lowerCAmelCase  							=      tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])       )

 # Preprocess test dataset
 lowerCAmelCase  							=      test_dataset.map(
     lambda _UpperCAmelCase       : tokenizer(e['text']   ,			truncation=_UpperCAmelCase   ,			padding='max_length'       )   ,			batched=_UpperCAmelCase       )
 test_dataset.set_format(type='tensorflow'   ,			columns=['input_ids', 'attention_mask', 'label']       )

 lowerCAmelCase  							=      {
     x: test_dataset[x].to_tensor(default_value=0   ,			shape=[None, tokenizer.model_max_length]       )
     for x in ['input_ids', 'attention_mask']
 }
 lowerCAmelCase  							=      tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])       )

 if SDP_ENABLED:
  lowerCAmelCase  							=      tf_train_dataset.shard(sdp.size()   ,			sdp.rank()       )
  lowerCAmelCase  							=      tf_test_dataset.shard(sdp.size()   ,			sdp.rank()       )
 lowerCAmelCase  							=      tf_train_dataset.batch(_UpperCAmelCase   ,			drop_remainder=_UpperCAmelCase       )
 lowerCAmelCase  							=      tf_test_dataset.batch(_UpperCAmelCase   ,			drop_remainder=_UpperCAmelCase       )

 return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
       __UpperCamelCase			:		List[Any]		    =   argparse.ArgumentParser()

       # Hyperparameters sent by the client are passed as command-line arguments to the script.
       parser.add_argument('''--epochs''', type=int, default=3)
       parser.add_argument('''--per_device_train_batch_size''', type=int, default=16)
       parser.add_argument('''--per_device_eval_batch_size''', type=int, default=8)
       parser.add_argument('''--model_name_or_path''', type=str)
       parser.add_argument('''--learning_rate''', type=str, default=5e-5)
       parser.add_argument('''--do_train''', type=bool, default=True)
       parser.add_argument('''--do_eval''', type=bool, default=True)
       parser.add_argument('''--output_dir''', type=str)
       parser.add_argument('''--max_steps''', type=int, default=None)

       # Data, model, and output directories
       parser.add_argument('''--output_data_dir''', type=str, default=os.environ['''SM_OUTPUT_DATA_DIR'''])
       parser.add_argument('''--model_dir''', type=str, default=os.environ['''SM_MODEL_DIR'''])
       parser.add_argument('''--n_gpus''', type=str, default=os.environ['''SM_NUM_GPUS'''])

       __UpperCamelCase    ,__UpperCamelCase			:		str		    =   parser.parse_known_args()

       # Set up logging
       __UpperCamelCase			:		Union[str, Any]		    =   logging.getLogger(__name__)

       logging.basicConfig(
           level=logging.getLevelName('''INFO'''),
           handlers=[logging.StreamHandler(sys.stdout)],
           format='''%(asctime)s - %(name)s - %(levelname)s - %(message)s''',
       )

       if SDP_ENABLED:
              sdp.init()

              __UpperCamelCase			:		Any		    =   tf.config.experimental.list_physical_devices('''GPU''')
              for gpu in gpus:
                     tf.config.experimental.set_memory_growth(gpu, True)
              if gpus:
                     tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], '''GPU''')

    # Load model and tokenizer
       __UpperCamelCase			:		List[str]		    =   TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
       __UpperCamelCase			:		Union[str, Any]		    =   AutoTokenizer.from_pretrained(args.model_name_or_path)

       # get datasets
       __UpperCamelCase    ,__UpperCamelCase			:		int		    =   get_datasets(
           tokenizer=tokenizer,
           train_batch_size=args.per_device_train_batch_size,
           eval_batch_size=args.per_device_eval_batch_size,
       )

       # fine optimizer and loss
       __UpperCamelCase			:		Tuple		    =   tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
       __UpperCamelCase			:		Union[str, Any]		    =   tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
       __UpperCamelCase			:		Dict		    =   [tf.keras.metrics.SparseCategoricalAccuracy()]
       model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

       # Training
       if args.do_train:
              # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
              __UpperCamelCase			:		Any		    =   time.time()
              __UpperCamelCase			:		List[Any]		    =   fit(
                  model,
                  loss,
                  optimizer,
                  tf_train_dataset,
                  args.epochs,
                  args.per_device_train_batch_size,
                  max_steps=args.max_steps,
              )
              __UpperCamelCase			:		Tuple		    =   time.time() - start_train_time
              logger.info('''*** Train ***''')
              logger.info(f'''train_runtime = {end_train_time}''')

              __UpperCamelCase			:		Dict		    =   os.path.join(args.output_dir, '''train_results.txt''')

              if not SDP_ENABLED or sdp.rank() == 0:
                     with open(output_eval_file, '''w''') as writer:
                            logger.info('''***** Train results *****''')
                            logger.info(train_results)
                            for key, value in train_results.items():
                                   logger.info(f'''  {key} = {value}''')
                                   writer.write(f'''{key} = {value}\n''')

    # Evaluation
       if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
              __UpperCamelCase			:		Tuple		    =   model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
              logger.info('''*** Evaluate ***''')

              __UpperCamelCase			:		Union[str, Any]		    =   os.path.join(args.output_dir, '''eval_results.txt''')

              with open(output_eval_file, '''w''') as writer:
                     logger.info('''***** Eval results *****''')
                     logger.info(result)
                     for key, value in result.items():
                            logger.info(f'''  {key} = {value}''')
                            writer.write(f'''{key} = {value}\n''')

    # Save result
       if SDP_ENABLED:
              if sdp.rank() == 0:
                     model.save_pretrained(args.output_dir)
                     tokenizer.save_pretrained(args.output_dir)
       else:
              model.save_pretrained(args.output_dir)
              tokenizer.save_pretrained(args.output_dir)












"""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]			)





