






from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed






def  lowerCamelCase__ (_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase):
			for param, grad_param in zip(model_a.parameters()							,				model_b.parameters()):
						if not param.requires_grad:
									continue
						if not did_step:
									# Grads should not be in sync
									assert (
									    torch.allclose(param.grad							,				grad_param.grad) is False
									), F'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
						else:
									# Grads should be in sync
									assert (
									    torch.allclose(param.grad							,				grad_param.grad) is True
									), F'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''






def  lowerCamelCase__ (_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase=True):
			model.train()
			SCREAMING_SNAKE_CASE						 =       model(__lowerCamelCase)
			SCREAMING_SNAKE_CASE						 =       F.mse_loss(__lowerCamelCase							,				target.to(output.device))
			if not do_backward:
						loss /= accelerator.gradient_accumulation_steps
						loss.backward()
			else:
						accelerator.backward(__lowerCamelCase)






def  lowerCamelCase__ (_UpperCAmelCase							,				_UpperCAmelCase=False):
			set_seed(42)
			SCREAMING_SNAKE_CASE						 =       RegressionModel()
			SCREAMING_SNAKE_CASE						 =       deepcopy(__lowerCamelCase)
			SCREAMING_SNAKE_CASE						 =       RegressionDataset(length=80)
			SCREAMING_SNAKE_CASE						 =       DataLoader(__lowerCamelCase							,				batch_size=16)
			model.to(accelerator.device)
			if sched:
						SCREAMING_SNAKE_CASE						 =       AdamW(params=model.parameters()							,				lr=1e-3)
						SCREAMING_SNAKE_CASE						 =       AdamW(params=ddp_model.parameters()							,				lr=1e-3)
						SCREAMING_SNAKE_CASE						 =       LambdaLR(__lowerCamelCase							,				lr_lambda=lambda _UpperCAmelCase: epoch**0.65)
						SCREAMING_SNAKE_CASE						 =       LambdaLR(__lowerCamelCase							,				lr_lambda=lambda _UpperCAmelCase: epoch**0.65)
			# Make a copy of `model`
			if sched:
						SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       accelerator.prepare(__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase)
			else:
						SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       accelerator.prepare(__lowerCamelCase							,				__lowerCamelCase)
			if sched:
						return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
			return model, ddp_model, dataloader






def  lowerCamelCase__ (_UpperCAmelCase):
			# Test when on a single CPU or GPU that the context manager does nothing
			SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       get_training_setup(__lowerCamelCase)
			# Use a single batch
			SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       next(iter(__lowerCamelCase)).values()
			for iteration in range(3):
						# Gather the distributed inputs and targs for the base model
						SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       accelerator.gather((ddp_input, ddp_target))
						SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       input.to(accelerator.device), target.to(accelerator.device)
						# Perform our initial ground truth step in non "DDP"
						step_model(__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase)
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(__lowerCamelCase):
												step_model(__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase)
						else:
									# Sync grads
									step_model(__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase)

						# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
						check_model_parameters(__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase)
						for param, ddp_param in zip(model.parameters()							,				ddp_model.parameters()):
									if not param.requires_grad:
												continue
									assert torch.allclose(
									    param.grad							,				ddp_param.grad), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

						# Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration)
						SCREAMING_SNAKE_CASE						 =       ddp_input[torch.randperm(len(__lowerCamelCase))]






def  lowerCamelCase__ (_UpperCAmelCase):
			# Test on distributed setup that context manager behaves properly
			SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       get_training_setup(__lowerCamelCase)
			# Use a single batch
			SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       next(iter(__lowerCamelCase)).values()
			for iteration in range(3):
						# Gather the distributed inputs and targs for the base model
						SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       accelerator.gather((ddp_input, ddp_target))
						SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       input.to(accelerator.device), target.to(accelerator.device)
						# Perform our initial ground truth step in non "DDP"
						step_model(__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase)
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(__lowerCamelCase):
												step_model(__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase)
						else:
									# Sync grads
									step_model(__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase)

						# DDP model and model should only be in sync when not (iteration % 2 == 0)
						for param, ddp_param in zip(model.parameters()							,				ddp_model.parameters()):
									if not param.requires_grad:
												continue
									if iteration % 2 == 0:
												# Grads should not be in sync
												assert (
												    torch.allclose(param.grad							,				ddp_param.grad) is False
												), F'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
									else:
												# Grads should be in sync
												assert (
												    torch.allclose(param.grad							,				ddp_param.grad) is True
												), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration)
						SCREAMING_SNAKE_CASE						 =       ddp_input[torch.randperm(len(__lowerCamelCase))]






def  lowerCamelCase__ (_UpperCAmelCase=False							,				_UpperCAmelCase=False):
			SCREAMING_SNAKE_CASE						 =       Accelerator(
			    split_batches=__lowerCamelCase							,				dispatch_batches=__lowerCamelCase							,				gradient_accumulation_steps=2)
			# Test that context manager behaves properly
			SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       get_training_setup(__lowerCamelCase)
			for iteration, batch in enumerate(__lowerCamelCase):
						SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       batch.values()
						# Gather the distributed inputs and targs for the base model
						SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       accelerator.gather((ddp_input, ddp_target))
						SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       input.to(accelerator.device), target.to(accelerator.device)
						# Perform our initial ground truth step in non "DDP"
						step_model(__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase)
						# Do "gradient accumulation" (noop)
						with accelerator.accumulate(__lowerCamelCase):
									step_model(__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase)

						# DDP model and model should only be in sync when not (iteration % 2 == 0)
						for param, ddp_param in zip(model.parameters()							,				ddp_model.parameters()):
									if not param.requires_grad:
												continue
									if ((iteration + 1) % 2 == 0) or (iteration == len(__lowerCamelCase) - 1):
												# Grads should be in sync
												assert (
												    torch.allclose(param.grad							,				ddp_param.grad) is True
												), F'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
									else:
												# Grads should not be in sync
												assert (
												    torch.allclose(param.grad							,				ddp_param.grad) is False
												), F'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration)
						SCREAMING_SNAKE_CASE						 =       ddp_input[torch.randperm(len(__lowerCamelCase))]
			GradientState._reset_state()






def  lowerCamelCase__ (_UpperCAmelCase=False							,				_UpperCAmelCase=False):
			SCREAMING_SNAKE_CASE						 =       Accelerator(
			    split_batches=__lowerCamelCase							,				dispatch_batches=__lowerCamelCase							,				gradient_accumulation_steps=2)
			# Test that context manager behaves properly
			SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       get_training_setup(__lowerCamelCase							,				__lowerCamelCase)
			for iteration, batch in enumerate(__lowerCamelCase):
						SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       batch.values()
						# Gather the distributed inputs and targs for the base model
						SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       accelerator.gather((ddp_input, ddp_target))
						SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       input.to(accelerator.device), target.to(accelerator.device)
						# Perform our initial ground truth step in non "DDP"
						model.train()
						ddp_model.train()
						step_model(__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase)
						opt.step()

						if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__lowerCamelCase)):
									if split_batches:
												sched.step()
									else:
												for _ in range(accelerator.num_processes):
															sched.step()
						opt.zero_grad()
						# Perform gradient accumulation under wrapper
						with accelerator.accumulate(__lowerCamelCase):
									step_model(__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase)
									ddp_opt.step()
									ddp_sched.step()
									ddp_opt.zero_grad()

						# Learning rates should be the same
						assert (
						    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
						), F'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n'''
						SCREAMING_SNAKE_CASE						 =       (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__lowerCamelCase))
						if accelerator.num_processes > 1:
									check_model_parameters(__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase							,				__lowerCamelCase)
						# Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration)
			GradientState._reset_state()






def  lowerCamelCase__ ():
			SCREAMING_SNAKE_CASE						 =       Accelerator()

			SCREAMING_SNAKE_CASE						 =       RegressionDataset(length=80)
			SCREAMING_SNAKE_CASE						 =       DataLoader(__lowerCamelCase							,				batch_size=16)
			SCREAMING_SNAKE_CASE						 =       RegressionDataset(length=96)
			SCREAMING_SNAKE_CASE						 =       DataLoader(__lowerCamelCase							,				batch_size=16)
			SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE						 =       accelerator.prepare(__lowerCamelCase							,				__lowerCamelCase)
			assert accelerator.gradient_state.active_dataloader is None
			for iteration, _ in enumerate(__lowerCamelCase):
						assert id(accelerator.gradient_state.active_dataloader) == id(__lowerCamelCase)
						if iteration < len(__lowerCamelCase) - 1:
									assert not accelerator.gradient_state.end_of_dataloader
									if iteration == 1:
												for batch_num, _ in enumerate(__lowerCamelCase):
															assert id(accelerator.gradient_state.active_dataloader) == id(__lowerCamelCase)
															if batch_num < len(__lowerCamelCase) - 1:
																		assert not accelerator.gradient_state.end_of_dataloader
															else:
																		assert accelerator.gradient_state.end_of_dataloader
						else:
									assert accelerator.gradient_state.end_of_dataloader
			assert accelerator.gradient_state.active_dataloader is None






def  lowerCamelCase__ ():
			SCREAMING_SNAKE_CASE						 =       Accelerator()
			SCREAMING_SNAKE_CASE						 =       accelerator.state
			if state.local_process_index == 0:
						print('**Test `accumulate` gradient accumulation with dataloader break**')
			test_dataloader_break()
			if state.distributed_type == DistributedType.NO:
						if state.local_process_index == 0:
									print('**Test NOOP `no_sync` context manager**')
						test_noop_sync(__lowerCamelCase)
			if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
						if state.local_process_index == 0:
									print('**Test Distributed `no_sync` context manager**')
						test_distributed_sync(__lowerCamelCase)
			if state.distributed_type == DistributedType.MULTI_GPU:
						for split_batch in [True, False]:
									for dispatch_batches in [True, False]:
												if state.local_process_index == 0:
															print(
															    '**Test `accumulate` gradient accumulation, '							,				F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''							,				)
												test_gradient_accumulation(__lowerCamelCase							,				__lowerCamelCase)

    # Currently will break on torch 2.0 +, need to investigate why
			if is_torch_version('<'							,				'2.0') or state.distributed_type == DistributedType.NO:
						if state.local_process_index == 0:
									print(
									    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '							,				'`split_batches=False`, `dispatch_batches=False`**'							,				)
						test_gradient_accumulation_with_opt_and_scheduler()
						if state.distributed_type == DistributedType.MULTI_GPU:
									for split_batch in [True, False]:
												for dispatch_batches in [True, False]:
															if not split_batch and not dispatch_batches:
																		continue
															if state.local_process_index == 0:
																		print(
																		    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '							,				F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''							,				)
															test_gradient_accumulation_with_opt_and_scheduler(__lowerCamelCase							,				__lowerCamelCase)






def  lowerCamelCase__ (_UpperCAmelCase):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
				main()










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase					:       Optional[Any]    					=      {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       Tuple    					=      [
		    "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "JukeboxModel",
		    "JukeboxPreTrainedModel",
		    "JukeboxVQVAE",
		    "JukeboxPrior",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		__UpperCamelCase					:       List[Any]    					=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

