

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase_ 			=			16
lowerCAmelCase_ 			=			32


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						# Initialize accelerator
						if args.with_tracking:
												snake_case_     =			Accelerator(
												    cpu=args.cpu						,	mixed_precision=args.mixed_precision						,	log_with='''all'''						,	project_dir=args.project_dir )
						else:
												snake_case_     =			Accelerator(cpu=args.cpu						,	mixed_precision=args.mixed_precision )

						if hasattr(args.checkpointing_steps						,	'''isdigit''' ):
												if args.checkpointing_steps == "epoch":
																		snake_case_     =			args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		snake_case_     =			int(args.checkpointing_steps )
												else:
																		raise ValueError(
																		    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''' )
						else:
												snake_case_     =			None
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						snake_case_     =			config['''lr''']
						snake_case_     =			int(config['''num_epochs'''] )
						snake_case_     =			int(config['''seed'''] )
						snake_case_     =			int(config['''batch_size'''] )

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												snake_case_     =			os.path.split(lowercase__ )[-1].split('''.''' )[0]
												accelerator.init_trackers(lowercase__						,	lowercase__ )

						snake_case_     =			AutoTokenizer.from_pretrained('''bert-base-cased''' )
						snake_case_     =			load_dataset('''glue'''						,	'''mrpc''' )
						snake_case_     =			evaluate.load('''glue'''						,	'''mrpc''' )

						def tokenize_function(SCREAMING_SNAKE_CASE__ ):
												# max_length=None => use the model max length (it's actually the default)
												snake_case_     =			tokenizer(examples['''sentence1''']						,	examples['''sentence2''']						,	truncation=lowercase__						,	max_length=lowercase__ )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												snake_case_     =			datasets.map(
												    lowercase__						,	batched=lowercase__						,	remove_columns=['''idx''', '''sentence1''', '''sentence2''']						,	)

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						snake_case_     =			tokenized_datasets.rename_column('''label'''						,	'''labels''' )

						# If the batch size is too big we use gradient accumulation
						snake_case_     =			1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												snake_case_     =			batch_size // MAX_GPU_BATCH_SIZE
												snake_case_     =			MAX_GPU_BATCH_SIZE

						def collate_fn(SCREAMING_SNAKE_CASE__ ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												snake_case_     =			128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		snake_case_     =			16
												elif accelerator.mixed_precision != "no":
																		snake_case_     =			8
												else:
																		snake_case_     =			None

												return tokenizer.pad(
												    lowercase__						,	padding='''longest'''						,	max_length=lowercase__						,	pad_to_multiple_of=lowercase__						,	return_tensors='''pt'''						,	)

						# Instantiate dataloaders.
						snake_case_     =			DataLoader(
						    tokenized_datasets['''train''']						,	shuffle=lowercase__						,	collate_fn=lowercase__						,	batch_size=lowercase__ )
						snake_case_     =			DataLoader(
						    tokenized_datasets['''validation''']						,	shuffle=lowercase__						,	collate_fn=lowercase__						,	batch_size=lowercase__ )

						set_seed(lowercase__ )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						snake_case_     =			AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''						,	return_dict=lowercase__ )

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						snake_case_     =			model.to(accelerator.device )

						# Instantiate optimizer
						snake_case_     =			AdamW(params=model.parameters()						,	lr=lowercase__ )

						# Instantiate scheduler
						snake_case_     =			get_linear_schedule_with_warmup(
						    optimizer=lowercase__						,	num_warmup_steps=100						,	num_training_steps=(len(lowercase__ ) * num_epochs) // gradient_accumulation_steps						,	)

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						snake_case_,					snake_case_,					snake_case_,					snake_case_,					snake_case_     =			accelerator.prepare(
						    lowercase__						,	lowercase__						,	lowercase__						,	lowercase__						,	lowercase__ )

						# We need to keep track of how many total steps we have iterated over
						snake_case_     =			0
						# We also need to keep track of the stating epoch so files are named properly
						snake_case_     =			0

						# Potentially load in the weights and states from a previous save
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}''' )
																		accelerator.load_state(args.resume_from_checkpoint )
																		snake_case_     =			os.path.basename(args.resume_from_checkpoint )
												else:
																		# Get the most recent checkpoint
																		snake_case_     =			[f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
																		dirs.sort(key=os.path.getctime )
																		snake_case_     =			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												snake_case_     =			os.path.splitext(lowercase__ )[0]

												if "epoch" in training_difference:
																		snake_case_     =			int(training_difference.replace('''epoch_'''						,	'''''' ) ) + 1
																		snake_case_     =			None
												else:
																		snake_case_     =			int(training_difference.replace('''step_'''						,	'''''' ) )
																		snake_case_     =			resume_step // len(lowercase__ )
																		resume_step -= starting_epoch * len(lowercase__ )

    # Now we train the model
						for epoch in range(lowercase__						,	lowercase__ ):
												model.train()
												if args.with_tracking:
																		snake_case_     =			0
												if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																		# We need to skip steps until we reach the resumed step
																		snake_case_     =			accelerator.skip_first_batches(lowercase__						,	lowercase__ )
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		snake_case_     =			train_dataloader
												for step, batch in enumerate(lowercase__ ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device )
																		snake_case_     =			model(**lowercase__ )
																		snake_case_     =			outputs.loss
																		snake_case_     =			loss / gradient_accumulation_steps
																		# We keep track of the loss at each epoch
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		accelerator.backward(lowercase__ )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

																		if isinstance(lowercase__						,	lowercase__ ):
																								snake_case_     =			F'''step_{overall_step}'''
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				snake_case_     =			os.path.join(args.output_dir						,	lowercase__ )
																														accelerator.save_state(lowercase__ )

												model.eval()
												for step, batch in enumerate(lowercase__ ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device )
																		with torch.no_grad():
																								snake_case_     =			model(**lowercase__ )
																		snake_case_     =			outputs.logits.argmax(dim=-1 )
																		snake_case_,					snake_case_     =			accelerator.gather_for_metrics((predictions, batch['''labels''']) )
																		metric.add_batch(
																		    predictions=lowercase__						,	references=lowercase__						,	)

												snake_case_     =			metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''						,	lowercase__ )
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        '''accuracy''': eval_metric['''accuracy'''],
																		        '''f1''': eval_metric['''f1'''],
																		        '''train_loss''': total_loss.item() / len(lowercase__ ),
																		        '''epoch''': epoch,
																		    }						,	step=lowercase__						,	)

												if checkpointing_steps == "epoch":
																		snake_case_     =			F'''epoch_{epoch}'''
																		if args.output_dir is not None:
																								snake_case_     =			os.path.join(args.output_dir						,	lowercase__ )
																		accelerator.save_state(lowercase__ )

						if args.with_tracking:
												accelerator.end_training()


def 						__SCREAMING_SNAKE_CASE      ():
						snake_case_     =			argparse.ArgumentParser(description='''Simple example of training script.''' )
						parser.add_argument(
						    '''--mixed_precision'''						,	type=lowercase__						,	default=lowercase__						,	choices=['''no''', '''fp16''', '''bf16''', '''fp8''']						,	help='''Whether to use mixed precision. Choose'''
						    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
						    '''and an Nvidia Ampere GPU.'''						,	)
						parser.add_argument('''--cpu'''						,	action='''store_true'''						,	help='''If passed, will train on the CPU.''' )
						parser.add_argument(
						    '''--checkpointing_steps'''						,	type=lowercase__						,	default=lowercase__						,	help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''						,	)
						parser.add_argument(
						    '''--resume_from_checkpoint'''						,	type=lowercase__						,	default=lowercase__						,	help='''If the training should continue from a checkpoint folder.'''						,	)
						parser.add_argument(
						    '''--with_tracking'''						,	action='''store_true'''						,	help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''						,	)
						parser.add_argument(
						    '''--output_dir'''						,	type=lowercase__						,	default='''.'''						,	help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''						,	)
						parser.add_argument(
						    '''--project_dir'''						,	type=lowercase__						,	default='''logs'''						,	help='''Location on where to store experiment tracking logs` and relevent project information'''						,	)
						snake_case_     =			parser.parse_args()
						snake_case_     =			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(lowercase__						,	lowercase__ )


if __name__ == "__main__":
					main()


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 			snake_case_     (	unittest.TestCase      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       (JukeboxModel,) if is_torch_available() else ()
			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       "openai/jukebox-1b-lyrics"
			SCREAMING_SNAKE_CASE  :     Union[str, Any]		    =       {
			    "artist": "Zac Brown Band",
			    "genres": "Country",
			    "lyrics": "I met a traveller from an antique land,\n    Who said \"Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ",
			}
			# fmt: off
			SCREAMING_SNAKE_CASE  :     int		    =       [
			    1864, 1536, 1213, 1870, 1357, 1536, 519, 880, 1323, 789, 1082, 534,
			    1000, 1445, 1105, 1130, 967, 515, 1434, 1620, 534, 1495, 283, 1445,
			    333, 1307, 539, 1631, 1528, 375, 1434, 673, 627, 710, 778, 1883,
			    1405, 1276, 1455, 1228
			]

			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       [
			    1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653
			]

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

			SCREAMING_SNAKE_CASE  :     Tuple		    =       [
			    1755, 842, 307, 1843, 1022, 1395, 234, 1554, 806, 739, 1022, 442,
			    616, 556, 268, 1499, 933, 457, 1440, 1837, 755, 985, 308, 902,
			    293, 1443, 1671, 1141, 1533, 555, 1562, 1061, 287, 417, 1022, 2008,
			    1186, 1015, 1777, 268
			]
			SCREAMING_SNAKE_CASE  :     Union[str, Any]		    =       [
			    854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842,
			    185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842,
			    591, 842, 1353, 842, 185, 842, 591, 842, 591, 114, 591, 842,
			    185, 842, 591, 89
			]

			SCREAMING_SNAKE_CASE  :     Dict		    =       [1058304, 0, 786432, 7169, 507, 76, 27, 40, 30, 76]

			SCREAMING_SNAKE_CASE  :     str		    =       [
			    390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002,
			    1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002,
			    1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907,
			    1907, 1788, 596, 1626
			]
			SCREAMING_SNAKE_CASE  :     List[Any]		    =       [
			    1236, 1668, 1484, 1920, 1848, 1409, 139, 864, 1828, 1272, 1599, 824,
			    1672, 139, 555, 1484, 824, 1920, 555, 596, 1579, 1599, 1231, 1599,
			    1637, 1407, 212, 824, 1599, 116, 1433, 824, 258, 1599, 1433, 1895,
			    1063, 1433, 1433, 1599
			]
			SCREAMING_SNAKE_CASE  :     Tuple		    =       [
			    1684, 1873, 1119, 1189, 395, 611, 1901, 972, 890, 1337, 1392, 1927,
			    96, 972, 672, 780, 1119, 890, 158, 771, 1073, 1927, 353, 1331,
			    1269, 1459, 1333, 1645, 812, 1577, 1337, 606, 353, 981, 1466, 619,
			    197, 391, 302, 1930
			]
			SCREAMING_SNAKE_CASE  :     List[str]		    =       [
			    390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002,
			    1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002,
			    1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907,
			    1907, 1788, 596, 1626
			]
			SCREAMING_SNAKE_CASE  :     int		    =       [
			    -0.0492, -0.0524, -0.0565, -0.0640, -0.0686, -0.0684, -0.0677, -0.0664,
			    -0.0605, -0.0490, -0.0330, -0.0168, -0.0083, -0.0075, -0.0051, 0.0025,
			    0.0136, 0.0261, 0.0386, 0.0497, 0.0580, 0.0599, 0.0583, 0.0614,
			    0.0740, 0.0889, 0.1023, 0.1162, 0.1211, 0.1212, 0.1251, 0.1336,
			    0.1502, 0.1686, 0.1883, 0.2148, 0.2363, 0.2458, 0.2507, 0.2531
			]
			SCREAMING_SNAKE_CASE  :     Dict		    =       [
			    0.0256, -0.0544, 0.1600, -0.0032, 0.1066, 0.0825, -0.0013, 0.3440,
			    0.0210, 0.0412, -0.1777, -0.0892, -0.0164, 0.0285, -0.0613, -0.0617,
			    -0.0137, -0.0201, -0.0175, 0.0215, -0.0627, 0.0520, -0.0730, 0.0970,
			    -0.0100, 0.0442, -0.0586, 0.0207, -0.0015, -0.0082
			]
			SCREAMING_SNAKE_CASE  :     List[str]		    =       [
			    0.0415, 0.0877, 0.0022, -0.0055, 0.0751, 0.0334, 0.0324, -0.0068,
			    0.0011, 0.0017, -0.0676, 0.0655, -0.0143, 0.0399, 0.0303, 0.0743,
			    -0.0168, -0.0394, -0.1113, 0.0124, 0.0442, 0.0267, -0.0003, -0.1536,
			    -0.0116, -0.1837, -0.0180, -0.1026, -0.0777, -0.0456
			]
			SCREAMING_SNAKE_CASE  :     Any		    =       [
			    76, 27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45, 45, 76, 38, 31, 33,
			    45, 76, 41, 32, 76, 45, 46, 41, 40, 31, 78, 76
			]







			def        snake_case__(  self				:       Union[str, Any]							)   ->str:
									snake_case_     =			JukeboxTokenizer.from_pretrained(self.model_id							)
									snake_case_     =			tokenizer(**self.metas							)['''input_ids''']
									return tokens







			@slow
			def        snake_case__(  self				:       int							)   ->str:
									snake_case_     =			JukeboxModel.from_pretrained(self.model_id							,     min_duration=0							).eval()
									snake_case_     =			self.prepare_inputs()

									set_seed(0							)
									snake_case_     =			[torch.zeros(1							,     0							,     dtype=torch.long							).cpu() for _ in range(3							)]
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [0]							,     sample_length=4_0 * model.priors[0].raw_to_tokens							,     save_results=_UpperCamelCase							)
									self.assertIn(zs[0][0].detach().cpu().tolist()							,     [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]							)

									set_seed(0							)
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [1]							,     sample_length=4_0 * model.priors[1].raw_to_tokens							,     save_results=_UpperCamelCase							)
									self.assertIn(zs[1][0].detach().cpu().tolist()							,     [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]							)

									set_seed(0							)
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [2]							,     sample_length=4_0 * model.priors[2].raw_to_tokens							,     save_results=_UpperCamelCase							)
									self.assertIn(zs[2][0].detach().cpu().tolist()							,     [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]							)







			@slow
			def        snake_case__(  self				:       Union[str, Any]							)   ->List[str]:
									snake_case_     =			False
									snake_case_     =			JukeboxModel.from_pretrained(self.model_id							,     min_duration=0							).eval()

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

									snake_case_     =			model.priors[0]
									snake_case_     =			0
									snake_case_     =			top_prior.get_music_tokens_conds(_UpperCamelCase							,     start=_UpperCamelCase							,     end=start + top_prior.n_ctx							)
									snake_case_     =			top_prior.get_metadata(labels[0].clone()							,     _UpperCamelCase							,     1_0_5_8_3_0_4							,     0							)

									self.assertIsNone(_UpperCamelCase							)
									self.assertListEqual(metadata.numpy()[0][:1_0].tolist()							,     self.EXPECTED_Y_COND							)

									snake_case_,					snake_case_,					snake_case_     =			top_prior.get_cond(_UpperCamelCase							,     _UpperCamelCase							)
									torch.testing.assert_allclose(
									    audio_conditioning[0][0][:3_0].detach()							,     torch.tensor(self.EXPECTED_AUDIO_COND							)							,     atol=1e-4							,     rtol=1e-4							)
									torch.testing.assert_allclose(
									    metadata_conditioning[0][0][:3_0].detach()							,     torch.tensor(self.EXPECTED_META_COND							)							,     atol=1e-4							,     rtol=1e-4							)
									torch.testing.assert_allclose(
									    lyric_tokens[0, :3_0].detach()							,     torch.tensor(self.EXPECTED_LYRIC_COND							)							,     atol=1e-4							,     rtol=1e-4							)







			@slow
			def        snake_case__(  self				:       Union[str, Any]							)   ->List[Any]:
									snake_case_     =			False

									snake_case_     =			JukeboxModel.from_pretrained(self.model_id							,     min_duration=0							).eval()
									set_seed(0							)
									snake_case_     =			torch.rand((1, 5_1_2_0, 1)							)
									snake_case_     =			list(self.prepare_inputs()							)

									snake_case_     =			[model.vqvae.encode(_UpperCamelCase							,     start_level=2							,     bs_chunks=waveform.shape[0]							)[0], None, None]
									snake_case_     =			model._sample(
									    _UpperCamelCase							,     _UpperCamelCase							,     sample_levels=[0]							,     save_results=_UpperCamelCase							,     sample_length=4_0 * model.priors[0].raw_to_tokens							)
									torch.testing.assert_allclose(zs[0][0][:4_0]							,     torch.tensor(self.EXPECTED_PRIMED_0							)							)

									snake_case_     =			torch.cat((zs[0], torch.zeros(1							,     2_0_4_8 - zs[0].shape[-1]							))							,     dim=-1							).long()
									snake_case_     =			[upper_a, model.vqvae.encode(_UpperCamelCase							,     start_level=1							,     bs_chunks=waveform.shape[0]							)[0], None]
									snake_case_     =			model._sample(
									    _UpperCamelCase							,     _UpperCamelCase							,     sample_levels=[1]							,     save_results=_UpperCamelCase							,     sample_length=4_0 * model.priors[1].raw_to_tokens							)
									torch.testing.assert_allclose(zs[1][0][:4_0]							,     torch.tensor(self.EXPECTED_PRIMED_1							)							)

									snake_case_     =			torch.cat((zs[1], torch.zeros(1							,     2_0_4_8 - zs[1].shape[-1]							))							,     dim=-1							).long()
									snake_case_     =			[upper_a, upper_a, model.vqvae.encode(_UpperCamelCase							,     start_level=0							,     bs_chunks=waveform.shape[0]							)[0]]
									snake_case_     =			model._sample(
									    _UpperCamelCase							,     _UpperCamelCase							,     sample_levels=[2]							,     save_results=_UpperCamelCase							,     sample_length=4_0 * model.priors[2].raw_to_tokens							)
									torch.testing.assert_allclose(zs[2][0][:4_0].cpu()							,     torch.tensor(self.EXPECTED_PRIMED_2							)							)







			@slow
			def        snake_case__(  self				:       Union[str, Any]							)   ->Optional[Any]:
									snake_case_     =			JukeboxModel.from_pretrained(self.model_id							,     min_duration=0							).eval()
									set_seed(0							)
									snake_case_     =			torch.rand((1, 5_1_2_0, 1)							)
									with torch.no_grad():
															snake_case_     =			model.vqvae.encode(_UpperCamelCase							,     start_level=2							,     bs_chunks=x.shape[0]							)
									torch.testing.assert_allclose(zs[0][0]							,     torch.tensor(self.EXPECTED_VQVAE_ENCODE							)							)

									with torch.no_grad():
															snake_case_     =			model.vqvae.decode(_UpperCamelCase							,     start_level=2							,     bs_chunks=x.shape[0]							)
									torch.testing.assert_allclose(x[0, :4_0, 0]							,     torch.tensor(self.EXPECTED_VQVAE_DECODE							)							,     atol=1e-4							,     rtol=1e-4							)






@require_torch
class 			snake_case_     (	unittest.TestCase      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     Any		    =       (JukeboxModel,) if is_torch_available() else ()
			SCREAMING_SNAKE_CASE  :     Optional[Any]		    =       "openai/jukebox-5b-lyrics"
			SCREAMING_SNAKE_CASE  :     int		    =       {
			    "artist": "Zac Brown Band",
			    "genres": "Country",
			    "lyrics": "I met a traveller from an antique land,\n    Who said \"Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ",
			}

			# fmt: off
			SCREAMING_SNAKE_CASE  :     str		    =       [
			    1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    1489, 1489, 1489, 1489, 1150, 1853, 1509, 1150, 1357, 1509, 6, 1272
			]
			SCREAMING_SNAKE_CASE  :     List[str]		    =       [
			    1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
			    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
			]

			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       [
			    1125, 416, 1125, 1125, 1125, 1125, 1125, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
			]
			SCREAMING_SNAKE_CASE  :     Dict		    =       [
			    416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416
			]

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

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

			SCREAMING_SNAKE_CASE  :     List[str]		    =       [
			    1125, 1125, 416, 1125, 1125, 416, 1125, 1125, 416, 416, 1125, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
			    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
			]
			SCREAMING_SNAKE_CASE  :     Optional[Any]		    =       [
			    491, 1755, 34, 1613, 1755, 417, 992, 1613, 222, 842, 1353, 1613,
			    844, 632, 185, 1613, 844, 632, 185, 1613, 185, 842, 677, 1613,
			    185, 114, 1353, 1613, 307, 89, 844, 1613, 307, 1332, 234, 1979,
			    307, 89, 1353, 616, 34, 842, 185, 842, 34, 842, 185, 842,
			    307, 114, 185, 89, 34, 1268, 185, 89, 34, 842, 185, 89
			]







			def        snake_case__(  self				:       List[Any]							,     _UpperCamelCase				:       List[str]							)   ->List[Any]:
									snake_case_     =			JukeboxTokenizer.from_pretrained(_UpperCamelCase							)
									snake_case_     =			tokenizer(**self.metas							)['''input_ids''']
									return tokens







			@slow
			def        snake_case__(  self				:       List[Any]							)   ->Any:
									snake_case_     =			JukeboxModel.from_pretrained(self.model_id							,     min_duration=0							).eval()
									snake_case_     =			self.prepare_inputs(self.model_id							)

									set_seed(0							)
									snake_case_     =			[torch.zeros(1							,     0							,     dtype=torch.long							).cpu() for _ in range(3							)]
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [0]							,     sample_length=6_0 * model.priors[0].raw_to_tokens							,     save_results=_UpperCamelCase							)
									self.assertIn(zs[0][0].detach().cpu().tolist()							,     [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]							)

									set_seed(0							)
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [1]							,     sample_length=6_0 * model.priors[1].raw_to_tokens							,     save_results=_UpperCamelCase							)
									self.assertIn(zs[1][0].detach().cpu().tolist()							,     [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]							)

									set_seed(0							)
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [2]							,     sample_length=6_0 * model.priors[2].raw_to_tokens							,     save_results=_UpperCamelCase							)
									self.assertIn(zs[2][0].detach().cpu().tolist()							,     [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]							)







			@slow
			@skip('''Not enough GPU memory on CI runners'''							)
			def        snake_case__(  self				:       Optional[int]							)   ->List[str]:
									snake_case_     =			JukeboxModel.from_pretrained(self.model_id							,     min_duration=0							).eval()
									snake_case_     =			[i.cuda() for i in self.prepare_inputs(self.model_id							)]

									set_seed(0							)
									model.priors[0].cuda()
									snake_case_     =			[torch.zeros(1							,     0							,     dtype=torch.long							).cuda() for _ in range(3							)]
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [0]							,     sample_length=6_0 * model.priors[0].raw_to_tokens							,     save_results=_UpperCamelCase							)
									torch.testing.assert_allclose(zs[0][0].cpu()							,     torch.tensor(self.EXPECTED_GPU_OUTPUTS_2							)							)
									model.priors[0].cpu()

									set_seed(0							)
									model.priors[1].cuda()
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [1]							,     sample_length=6_0 * model.priors[1].raw_to_tokens							,     save_results=_UpperCamelCase							)
									torch.testing.assert_allclose(zs[1][0].cpu()							,     torch.tensor(self.EXPECTED_GPU_OUTPUTS_1							)							)
									model.priors[1].cpu()

									set_seed(0							)
									model.priors[2].cuda()
									snake_case_     =			model._sample(_UpperCamelCase							,     _UpperCamelCase							,     [2]							,     sample_length=6_0 * model.priors[2].raw_to_tokens							,     save_results=_UpperCamelCase							)
									torch.testing.assert_allclose(zs[2][0].cpu()							,     torch.tensor(self.EXPECTED_GPU_OUTPUTS_0							)							)







			@slow
			def        snake_case__(  self				:       List[Any]							)   ->List[Any]:
									snake_case_     =			'''ArthurZ/jukebox_prior_0'''
									snake_case_     =			JukeboxPrior.from_pretrained(_UpperCamelCase							,     min_duration=0							).eval().half().to('''cuda'''							)

									snake_case_     =			self.prepare_inputs(_UpperCamelCase							)[0].cuda()
									snake_case_     =			model.get_metadata(_UpperCamelCase							,     0							,     7_6_8_0							,     0							)
									set_seed(0							)
									snake_case_     =			model.sample(1							,     metadata=_UpperCamelCase							,     sample_tokens=6_0							)
									self.assertIn(outputs[0].cpu().tolist()							,     [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]							)