



"""simple docstring"""




import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

A__  :  Union[str, Any]       = 32


def       UpperCamelCase(  __UpperCamelCase							:		Any					,__UpperCamelCase							:		int = 16			):
		lowerCAmelCase_      :					str     =			AutoTokenizer.from_pretrained('''bert-base-cased'''			)
		lowerCAmelCase_      :					List[str]     =			load_dataset('''glue'''					,'''mrpc'''			)

		def tokenize_function(__UpperCamelCase							:		Optional[Any]			):
				# max_length=None => use the model max length (it's actually the default)
				lowerCAmelCase_      :					int     =			tokenizer(examples['''sentence1''']					,examples['''sentence2''']					,truncation=__UpperCamelCase					,max_length=__UpperCamelCase			)
				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():
				lowerCAmelCase_      :					Tuple     =			datasets.map(
				    __UpperCamelCase					,batched=__UpperCamelCase					,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
		lowerCAmelCase_      :					Union[str, Any]     =			tokenized_datasets.rename_column('''label'''					,'''labels'''			)

		def collate_fn(__UpperCamelCase							:		Dict			):
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						lowerCAmelCase_      :					str     =			16
				elif accelerator.mixed_precision != "no":
						lowerCAmelCase_      :					Dict     =			8
				else:
						lowerCAmelCase_      :					Dict     =			None

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

		# Instantiate dataloaders.
		lowerCAmelCase_      :					str     =			DataLoader(
		    tokenized_datasets['''train''']					,shuffle=__UpperCamelCase					,collate_fn=__UpperCamelCase					,batch_size=__UpperCamelCase			)
		lowerCAmelCase_      :					str     =			DataLoader(
		    tokenized_datasets['''validation''']					,shuffle=__UpperCamelCase					,collate_fn=__UpperCamelCase					,batch_size=__UpperCamelCase			)

		return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
						from accelerate.test_utils.training import mocked_dataloaders

						A__  :  Tuple       = mocked_dataloaders  # noqa: F811


def       UpperCamelCase(  __UpperCamelCase							:		List[str]					,__UpperCamelCase							:		Any			):







		if os.environ.get('''TESTING_MOCKED_DATALOADERS'''					,__UpperCamelCase			) == "1":
				lowerCAmelCase_      :					Optional[Any]     =			2
		# Initialize accelerator
		lowerCAmelCase_      :					Tuple     =			Accelerator(cpu=args.cpu					,mixed_precision=args.mixed_precision			)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowerCAmelCase_      :					Union[str, Any]     =			config['''lr''']
		lowerCAmelCase_      :					List[Any]     =			int(config['''num_epochs''']			)
		lowerCAmelCase_      :					Optional[Any]     =			int(config['''seed''']			)
		lowerCAmelCase_      :					Tuple     =			int(config['''batch_size''']			)

		lowerCAmelCase_      :					Optional[int]     =			evaluate.load('''glue'''					,'''mrpc'''			)

		# New Code #
		# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
		# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
		# half each time. From this, we can calculate the number of gradient accumulation steps needed
		# and modify the Accelerator object as a result
		@find_executable_batch_size(starting_batch_size=int(__UpperCamelCase			)			)
		def inner_training_loop(__UpperCamelCase							:		int			):
				# Since we need to modify the outside accelerator object, we need to bring it
				# to the local scope
				nonlocal accelerator

				# We can calculate the number of gradient accumulation steps based on the current
				# batch size vs the starting batch size
				lowerCAmelCase_      :					Tuple     =			observed_batch_size // batch_size

				# And then set it in the Accelerator directly:
				lowerCAmelCase_      :					Optional[int]     =			num_gradient_accumulation_steps

				# Next we need to free all of the stored model references in the Accelerator each time
				accelerator.free_memory()

				# And set the seed so our results are reproducable each reset
				set_seed(__UpperCamelCase			)

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

				# 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).
				lowerCAmelCase_      :					Optional[int]     =			model.to(accelerator.device			)

				# Instantiate optimizer
				lowerCAmelCase_      :					List[Any]     =			AdamW(params=model.parameters()					,lr=__UpperCamelCase			)
				lowerCAmelCase_ ,  lowerCAmelCase_      :					int     =			get_dataloaders(__UpperCamelCase					,__UpperCamelCase			)

				# Instantiate scheduler
				lowerCAmelCase_      :					Any     =			get_linear_schedule_with_warmup(
				    optimizer=__UpperCamelCase					,num_warmup_steps=100					,num_training_steps=(len(__UpperCamelCase			) * num_epochs)					,)

				# 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.
				lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_      :					List[str]     =			accelerator.prepare(
				    __UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase			)

				# Now we train the model
				for epoch in range(__UpperCamelCase			):
						model.train()
						for step, batch in enumerate(__UpperCamelCase			):
								# And perform gradient accumulation
								with accelerator.accumulate(__UpperCamelCase			):
										# We could avoid this line since we set the accelerator with `device_placement=True`.
										batch.to(accelerator.device			)
										lowerCAmelCase_      :					Optional[Any]     =			model(**__UpperCamelCase			)
										lowerCAmelCase_      :					Optional[Any]     =			outputs.loss
										accelerator.backward(__UpperCamelCase			)
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()

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

						lowerCAmelCase_      :					Union[str, Any]     =			metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"""epoch {epoch}:"""					,__UpperCamelCase			)

				# New Code #
				# And call it at the end with no arguments
				# Note: You could also refactor this outside of your training loop function
		inner_training_loop()


def       UpperCamelCase(  ):
		lowerCAmelCase_      :					int     =			argparse.ArgumentParser(description='''Simple example of training script.'''			)
		parser.add_argument(
		    '''--mixed_precision'''					,type=__UpperCamelCase					,default=__UpperCamelCase					,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.'''			)
		lowerCAmelCase_      :					Optional[int]     =			parser.parse_args()
		# New Code #
		# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
		lowerCAmelCase_      :					Any     =			{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
		training_function(__UpperCamelCase					,__UpperCamelCase			)


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						):
							_a      				=			(JukeboxModel,) if is_torch_available() else ()
							_a      				=			'''openai/jukebox-1b-lyrics'''
							_a      				=			{
							    '''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
							_a      				=			[
							    1_864, 1_536, 1_213, 1_870, 1_357, 1_536, 519, 880, 1_323, 789, 1_082, 534,
							    1_000, 1_445, 1_105, 1_130, 967, 515, 1_434, 1_620, 534, 1_495, 283, 1_445,
							    333, 1_307, 539, 1_631, 1_528, 375, 1_434, 673, 627, 710, 778, 1_883,
							    1_405, 1_276, 1_455, 1_228
							]

							_a      				=			[
							    1_489, 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
							]

							_a      				=			[
							    1_125, 1_751, 697, 1_776, 1_141, 1_476, 391, 697, 1_125, 684, 867, 416,
							    844, 1_372, 1_274, 717, 1_274, 844, 1_299, 1_419, 697, 1_370, 317, 1_125,
							    191, 1_440, 1_370, 1_440, 1_370, 282, 1_621, 1_370, 368, 349, 867, 1_872,
							    1_262, 869, 1_728, 747
							]
							_a      				=			[
							    416, 416, 1_125, 1_125, 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
							]

							_a      				=			[
							    1_755, 842, 307, 1_843, 1_022, 1_395, 234, 1_554, 806, 739, 1_022, 442,
							    616, 556, 268, 1_499, 933, 457, 1_440, 1_837, 755, 985, 308, 902,
							    293, 1_443, 1_671, 1_141, 1_533, 555, 1_562, 1_061, 287, 417, 1_022, 2_008,
							    1_186, 1_015, 1_777, 268
							]
							_a      				=			[
							    854, 842, 1_353, 114, 1_353, 842, 185, 842, 185, 114, 591, 842,
							    185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842,
							    591, 842, 1_353, 842, 185, 842, 591, 842, 591, 114, 591, 842,
							    185, 842, 591, 89
							]

							_a      				=			[1_058_304, 0, 786_432, 7_169, 507, 76, 27, 40, 30, 76]

							_a      				=			[
							    390, 1_160, 1_002, 1_907, 1_788, 1_788, 1_788, 1_907, 1_002, 1_002, 1_854, 1_002,
							    1_002, 1_002, 1_002, 1_002, 1_002, 1_160, 1_160, 1_606, 596, 596, 1_160, 1_002,
							    1_516, 596, 1_002, 1_002, 1_002, 1_907, 1_788, 1_788, 1_788, 1_854, 1_788, 1_907,
							    1_907, 1_788, 596, 1_626
							]
							_a      				=			[
							    1_236, 1_668, 1_484, 1_920, 1_848, 1_409, 139, 864, 1_828, 1_272, 1_599, 824,
							    1_672, 139, 555, 1_484, 824, 1_920, 555, 596, 1_579, 1_599, 1_231, 1_599,
							    1_637, 1_407, 212, 824, 1_599, 116, 1_433, 824, 258, 1_599, 1_433, 1_895,
							    1_063, 1_433, 1_433, 1_599
							]
							_a      				=			[
							    1_684, 1_873, 1_119, 1_189, 395, 611, 1_901, 972, 890, 1_337, 1_392, 1_927,
							    96, 972, 672, 780, 1_119, 890, 158, 771, 1_073, 1_927, 353, 1_331,
							    1_269, 1_459, 1_333, 1_645, 812, 1_577, 1_337, 606, 353, 981, 1_466, 619,
							    197, 391, 302, 1_930
							]
							_a      				=			[
							    390, 1_160, 1_002, 1_907, 1_788, 1_788, 1_788, 1_907, 1_002, 1_002, 1_854, 1_002,
							    1_002, 1_002, 1_002, 1_002, 1_002, 1_160, 1_160, 1_606, 596, 596, 1_160, 1_002,
							    1_516, 596, 1_002, 1_002, 1_002, 1_907, 1_788, 1_788, 1_788, 1_854, 1_788, 1_907,
							    1_907, 1_788, 596, 1_626
							]
							_a      				=			[
							    -0.0_4_9_2, -0.0_5_2_4, -0.0_5_6_5, -0.0_6_4_0, -0.0_6_8_6, -0.0_6_8_4, -0.0_6_7_7, -0.0_6_6_4,
							    -0.0_6_0_5, -0.0_4_9_0, -0.0_3_3_0, -0.0_1_6_8, -0.0_0_8_3, -0.0_0_7_5, -0.0_0_5_1, 0.0_0_2_5,
							    0.0_1_3_6, 0.0_2_6_1, 0.0_3_8_6, 0.0_4_9_7, 0.0_5_8_0, 0.0_5_9_9, 0.0_5_8_3, 0.0_6_1_4,
							    0.0_7_4_0, 0.0_8_8_9, 0.1_0_2_3, 0.1_1_6_2, 0.1_2_1_1, 0.1_2_1_2, 0.1_2_5_1, 0.1_3_3_6,
							    0.1_5_0_2, 0.1_6_8_6, 0.1_8_8_3, 0.2_1_4_8, 0.2_3_6_3, 0.2_4_5_8, 0.2_5_0_7, 0.2_5_3_1
							]
							_a      				=			[
							    0.0_2_5_6, -0.0_5_4_4, 0.1_6_0_0, -0.0_0_3_2, 0.1_0_6_6, 0.0_8_2_5, -0.0_0_1_3, 0.3_4_4_0,
							    0.0_2_1_0, 0.0_4_1_2, -0.1_7_7_7, -0.0_8_9_2, -0.0_1_6_4, 0.0_2_8_5, -0.0_6_1_3, -0.0_6_1_7,
							    -0.0_1_3_7, -0.0_2_0_1, -0.0_1_7_5, 0.0_2_1_5, -0.0_6_2_7, 0.0_5_2_0, -0.0_7_3_0, 0.0_9_7_0,
							    -0.0_1_0_0, 0.0_4_4_2, -0.0_5_8_6, 0.0_2_0_7, -0.0_0_1_5, -0.0_0_8_2
							]
							_a      				=			[
							    0.0_4_1_5, 0.0_8_7_7, 0.0_0_2_2, -0.0_0_5_5, 0.0_7_5_1, 0.0_3_3_4, 0.0_3_2_4, -0.0_0_6_8,
							    0.0_0_1_1, 0.0_0_1_7, -0.0_6_7_6, 0.0_6_5_5, -0.0_1_4_3, 0.0_3_9_9, 0.0_3_0_3, 0.0_7_4_3,
							    -0.0_1_6_8, -0.0_3_9_4, -0.1_1_1_3, 0.0_1_2_4, 0.0_4_4_2, 0.0_2_6_7, -0.0_0_0_3, -0.1_5_3_6,
							    -0.0_1_1_6, -0.1_8_3_7, -0.0_1_8_0, -0.1_0_2_6, -0.0_7_7_7, -0.0_4_5_6
							]
							_a      				=			[
							    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        UpperCAmelCase__      (	self					:	Tuple):
									lowerCAmelCase_      :					Any     =			JukeboxTokenizer.from_pretrained(self.model_id)
									lowerCAmelCase_      :					Union[str, Any]     =			tokenizer(**self.metas)['''input_ids''']
									return tokens




							@slow
							def        UpperCAmelCase__      (	self					:	Any):
									lowerCAmelCase_      :					Tuple     =			JukeboxModel.from_pretrained(self.model_id    ,    min_duration=0).eval()
									lowerCAmelCase_      :					Tuple     =			self.prepare_inputs()

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

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

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




							@slow
							def        UpperCAmelCase__      (	self					:	List[Any]):
									lowerCAmelCase_      :					int     =			False
									lowerCAmelCase_      :					Any     =			JukeboxModel.from_pretrained(self.model_id    ,    min_duration=0).eval()

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

									lowerCAmelCase_      :					List[str]     =			model.priors[0]
									lowerCAmelCase_      :					str     =			0
									lowerCAmelCase_      :					Dict     =			top_prior.get_music_tokens_conds(A_    ,    start=A_    ,    end=start + top_prior.n_ctx)
									lowerCAmelCase_      :					Dict     =			top_prior.get_metadata(labels[0].clone()    ,    A_    ,    1_0_5_8_3_0_4    ,    0)

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

									lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_      :					List[str]     =			top_prior.get_cond(A_    ,    A_)
									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        UpperCAmelCase__      (	self					:	Union[str, Any]):
									lowerCAmelCase_      :					int     =			False

									lowerCAmelCase_      :					Optional[Any]     =			JukeboxModel.from_pretrained(self.model_id    ,    min_duration=0).eval()
									set_seed(0)
									lowerCAmelCase_      :					Dict     =			torch.rand((1, 5_1_2_0, 1))
									lowerCAmelCase_      :					List[str]     =			list(self.prepare_inputs())

									lowerCAmelCase_      :					List[str]     =			[model.vqvae.encode(A_    ,    start_level=2    ,    bs_chunks=waveform.shape[0])[0], None, None]
									lowerCAmelCase_      :					Tuple     =			model._sample(
									    A_    ,    A_    ,    sample_levels=[0]    ,    save_results=A_    ,    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))

									lowerCAmelCase_      :					Dict     =			torch.cat((zs[0], torch.zeros(1    ,    2_0_4_8 - zs[0].shape[-1]))    ,    dim=-1).long()
									lowerCAmelCase_      :					Optional[int]     =			[upper_a, model.vqvae.encode(A_    ,    start_level=1    ,    bs_chunks=waveform.shape[0])[0], None]
									lowerCAmelCase_      :					List[Any]     =			model._sample(
									    A_    ,    A_    ,    sample_levels=[1]    ,    save_results=A_    ,    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))

									lowerCAmelCase_      :					Dict     =			torch.cat((zs[1], torch.zeros(1    ,    2_0_4_8 - zs[1].shape[-1]))    ,    dim=-1).long()
									lowerCAmelCase_      :					Tuple     =			[upper_a, upper_a, model.vqvae.encode(A_    ,    start_level=0    ,    bs_chunks=waveform.shape[0])[0]]
									lowerCAmelCase_      :					Optional[Any]     =			model._sample(
									    A_    ,    A_    ,    sample_levels=[2]    ,    save_results=A_    ,    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        UpperCAmelCase__      (	self					:	List[Any]):
									lowerCAmelCase_      :					str     =			JukeboxModel.from_pretrained(self.model_id    ,    min_duration=0).eval()
									set_seed(0)
									lowerCAmelCase_      :					List[Any]     =			torch.rand((1, 5_1_2_0, 1))
									with torch.no_grad():
											lowerCAmelCase_      :					Dict     =			model.vqvae.encode(A_    ,    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_      :					Dict     =			model.vqvae.decode(A_    ,    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						):
							_a      				=			(JukeboxModel,) if is_torch_available() else ()
							_a      				=			'''openai/jukebox-5b-lyrics'''
							_a      				=			{
							    '''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
							_a      				=			[
							    1_489, 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,
							    1_489, 1_489, 1_489, 1_489, 1_150, 1_853, 1_509, 1_150, 1_357, 1_509, 6, 1_272
							]
							_a      				=			[
							    1_489, 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
							]

							_a      				=			[
							    1_125, 416, 1_125, 1_125, 1_125, 1_125, 1_125, 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
							]
							_a      				=			[
							    416, 416, 1_125, 1_125, 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
							]

							_a      				=			[
							    1_755, 1_061, 234, 1_755, 1_061, 1_755, 185, 290, 307, 307, 616, 616,
							    616, 616, 616, 616, 307, 290, 417, 1_755, 234, 1_755, 185, 290,
							    290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1_755,
							    234, 234, 1_755, 234, 185, 185, 307, 616, 616, 616, 616, 290,
							    1_755, 1_755, 1_755, 234, 234, 1_755, 1_572, 290, 307, 616, 34, 616
							]
							_a      				=			[
							    854, 842, 1_353, 114, 1_353, 842, 185, 842, 185, 114, 591, 842, 185,
							    417, 185, 842, 307, 842, 591, 842, 185, 842, 185, 842, 591, 842,
							    1_353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591,
							    89, 591, 842, 591, 842, 591, 417, 1_372, 842, 1_372, 842, 34, 842,
							    185, 89, 591, 842, 185, 842, 591, 632
							]

							_a      				=			[
							    1_489, 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
							]
							_a      				=			[
							    1_489, 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, 1_853, 1_177, 1_536, 1_228,
							    710, 475, 1_489, 1_229, 1_224, 231, 1_224, 252, 1_434, 653, 475,
							    1_106, 1_877, 1_599, 1_228, 1_600, 1_683, 1_182, 1_853, 475, 1_864,
							    252, 1_229, 1_434, 2_001
							]

							_a      				=			[
							    1_125, 1_125, 416, 1_125, 1_125, 416, 1_125, 1_125, 416, 416, 1_125, 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
							]
							_a      				=			[
							    491, 1_755, 34, 1_613, 1_755, 417, 992, 1_613, 222, 842, 1_353, 1_613,
							    844, 632, 185, 1_613, 844, 632, 185, 1_613, 185, 842, 677, 1_613,
							    185, 114, 1_353, 1_613, 307, 89, 844, 1_613, 307, 1_332, 234, 1_979,
							    307, 89, 1_353, 616, 34, 842, 185, 842, 34, 842, 185, 842,
							    307, 114, 185, 89, 34, 1_268, 185, 89, 34, 842, 185, 89
							]




							def        UpperCAmelCase__      (	self					:	int    ,    A_					:	Union[str, Any]):
									lowerCAmelCase_      :					Tuple     =			JukeboxTokenizer.from_pretrained(A_)
									lowerCAmelCase_      :					Any     =			tokenizer(**self.metas)['''input_ids''']
									return tokens




							@slow
							def        UpperCAmelCase__      (	self					:	Dict):
									lowerCAmelCase_      :					Optional[Any]     =			JukeboxModel.from_pretrained(self.model_id    ,    min_duration=0).eval()
									lowerCAmelCase_      :					Union[str, Any]     =			self.prepare_inputs(self.model_id)

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

									set_seed(0)
									lowerCAmelCase_      :					Optional[int]     =			model._sample(A_    ,    A_    ,    [1]    ,    sample_length=6_0 * model.priors[1].raw_to_tokens    ,    save_results=A_)
									self.assertIn(zs[1][0].detach().cpu().tolist()    ,    [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2])

									set_seed(0)
									lowerCAmelCase_      :					Tuple     =			model._sample(A_    ,    A_    ,    [2]    ,    sample_length=6_0 * model.priors[2].raw_to_tokens    ,    save_results=A_)
									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					:	List[str]):
									lowerCAmelCase_      :					str     =			JukeboxModel.from_pretrained(self.model_id    ,    min_duration=0).eval()
									lowerCAmelCase_      :					Optional[Any]     =			[i.cuda() for i in self.prepare_inputs(self.model_id)]

									set_seed(0)
									model.priors[0].cuda()
									lowerCAmelCase_      :					Tuple     =			[torch.zeros(1    ,    0    ,    dtype=torch.long).cuda() for _ in range(3)]
									lowerCAmelCase_      :					Any     =			model._sample(A_    ,    A_    ,    [0]    ,    sample_length=6_0 * model.priors[0].raw_to_tokens    ,    save_results=A_)
									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_      :					Any     =			model._sample(A_    ,    A_    ,    [1]    ,    sample_length=6_0 * model.priors[1].raw_to_tokens    ,    save_results=A_)
									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_      :					Union[str, Any]     =			model._sample(A_    ,    A_    ,    [2]    ,    sample_length=6_0 * model.priors[2].raw_to_tokens    ,    save_results=A_)
									torch.testing.assert_allclose(zs[2][0].cpu()    ,    torch.tensor(self.EXPECTED_GPU_OUTPUTS_0))







							@slow
							def        UpperCAmelCase__      (	self					:	Optional[int]):
									lowerCAmelCase_      :					Tuple     =			'''ArthurZ/jukebox_prior_0'''
									lowerCAmelCase_      :					Any     =			JukeboxPrior.from_pretrained(A_    ,    min_duration=0).eval().half().to('''cuda''')

									lowerCAmelCase_      :					Any     =			self.prepare_inputs(A_)[0].cuda()
									lowerCAmelCase_      :					Any     =			model.get_metadata(A_    ,    0    ,    7_6_8_0    ,    0)
									set_seed(0)
									lowerCAmelCase_      :					Optional[int]     =			model.sample(1    ,    metadata=A_    ,    sample_tokens=6_0)
									self.assertIn(outputs[0].cpu().tolist()    ,    [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2])





