





"""simple docstring"""





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_      (   SCREAMING_SNAKE_CASE							:				List[Any]     ,    SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				Optional[Any]     ,    SCREAMING_SNAKE_CASE							:				Union[str, Any] ):


							'''simple docstring'''







							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_      (   SCREAMING_SNAKE_CASE							:				Any     ,    SCREAMING_SNAKE_CASE							:				Optional[Any]     ,    SCREAMING_SNAKE_CASE							:				Any     ,    SCREAMING_SNAKE_CASE							:				List[str]     ,    SCREAMING_SNAKE_CASE							:				Tuple=True ):


							'''simple docstring'''







							model.train()
							__lowerCamelCase   :   Dict       =model(UpperCamelCase__ )
							__lowerCamelCase   :   str       =F.mse_loss(UpperCamelCase__     ,    target.to(output.device ) )
							if not do_backward:
														loss /= accelerator.gradient_accumulation_steps
														loss.backward()
							else:
														accelerator.backward(UpperCamelCase__ )



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Tuple     ,    SCREAMING_SNAKE_CASE							:				List[Any]=False ):


							'''simple docstring'''







							set_seed(42 )
							__lowerCamelCase   :   Union[str, Any]       =RegressionModel()
							__lowerCamelCase   :   Optional[Any]       =deepcopy(UpperCamelCase__ )
							__lowerCamelCase   :   str       =RegressionDataset(length=80 )
							__lowerCamelCase   :   str       =DataLoader(UpperCamelCase__     ,    batch_size=16 )
							model.to(accelerator.device )
							if sched:
														__lowerCamelCase   :   str       =AdamW(params=model.parameters()     ,    lr=1E-3 )
														__lowerCamelCase   :   List[str]       =AdamW(params=ddp_model.parameters()     ,    lr=1E-3 )
														__lowerCamelCase   :   str       =LambdaLR(UpperCamelCase__     ,    lr_lambda=lambda SCREAMING_SNAKE_CASE : epoch**0.65 )
														__lowerCamelCase   :   Union[str, Any]       =LambdaLR(UpperCamelCase__     ,    lr_lambda=lambda SCREAMING_SNAKE_CASE : epoch**0.65 )
							# Make a copy of `model`
							if sched:
														__lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase   :   Dict       =accelerator.prepare(UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__ )
							else:
														__lowerCamelCase     ,    __lowerCamelCase   :   Optional[Any]       =accelerator.prepare(UpperCamelCase__     ,    UpperCamelCase__ )
							if sched:
														return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
							return model, ddp_model, dataloader



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Tuple ):


							'''simple docstring'''







							__lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase   :   Optional[int]       =get_training_setup(UpperCamelCase__ )
							# Use a single batch
							__lowerCamelCase     ,    __lowerCamelCase   :   int       =next(iter(UpperCamelCase__ ) ).values()
							for iteration in range(3 ):
														# Gather the distributed inputs and targs for the base model
														__lowerCamelCase     ,    __lowerCamelCase   :   Optional[int]       =accelerator.gather((ddp_input, ddp_target) )
														__lowerCamelCase     ,    __lowerCamelCase   :   Tuple       =input.to(accelerator.device ), target.to(accelerator.device )
														# Perform our initial ground truth step in non "DDP"
														step_model(UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__ )
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(UpperCamelCase__ ):
																												step_model(UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__ )
														else:
																					# Sync grads
																					step_model(UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__ )

														# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
														check_model_parameters(UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__ )
														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 )
														__lowerCamelCase   :   Optional[Any]       =ddp_input[torch.randperm(len(UpperCamelCase__ ) )]



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Optional[Any] ):


							'''simple docstring'''







							__lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase   :   Tuple       =get_training_setup(UpperCamelCase__ )
							# Use a single batch
							__lowerCamelCase     ,    __lowerCamelCase   :   Tuple       =next(iter(UpperCamelCase__ ) ).values()
							for iteration in range(3 ):
														# Gather the distributed inputs and targs for the base model
														__lowerCamelCase     ,    __lowerCamelCase   :   Optional[Any]       =accelerator.gather((ddp_input, ddp_target) )
														__lowerCamelCase     ,    __lowerCamelCase   :   Any       =input.to(accelerator.device ), target.to(accelerator.device )
														# Perform our initial ground truth step in non "DDP"
														step_model(UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__ )
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(UpperCamelCase__ ):
																												step_model(UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__ )
														else:
																					# Sync grads
																					step_model(UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__ )

														# 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 )
														__lowerCamelCase   :   Dict       =ddp_input[torch.randperm(len(UpperCamelCase__ ) )]



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int=False     ,    SCREAMING_SNAKE_CASE							:				Union[str, Any]=False ):


							'''simple docstring'''







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

														# 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(UpperCamelCase__ ) - 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 )
														__lowerCamelCase   :   List[Any]       =ddp_input[torch.randperm(len(UpperCamelCase__ ) )]
							GradientState._reset_state()



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Tuple=False     ,    SCREAMING_SNAKE_CASE							:				List[str]=False ):


							'''simple docstring'''







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

														if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCamelCase__ )):
																					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(UpperCamelCase__ ):
																					step_model(UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__ )
																					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'
														__lowerCamelCase   :   Union[str, Any]       =(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCamelCase__ ))
														if accelerator.num_processes > 1:
																					check_model_parameters(UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__ )
														# Shuffle ddp_input on each iteration
														torch.manual_seed(1337 + iteration )
							GradientState._reset_state()



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							__lowerCamelCase   :   Optional[int]       =Accelerator()

							__lowerCamelCase   :   Dict       =RegressionDataset(length=80 )
							__lowerCamelCase   :   Optional[int]       =DataLoader(UpperCamelCase__     ,    batch_size=16 )
							__lowerCamelCase   :   int       =RegressionDataset(length=96 )
							__lowerCamelCase   :   List[str]       =DataLoader(UpperCamelCase__     ,    batch_size=16 )
							__lowerCamelCase     ,    __lowerCamelCase   :   int       =accelerator.prepare(UpperCamelCase__     ,    UpperCamelCase__ )
							assert accelerator.gradient_state.active_dataloader is None
							for iteration, _ in enumerate(UpperCamelCase__ ):
														assert id(accelerator.gradient_state.active_dataloader ) == id(UpperCamelCase__ )
														if iteration < len(UpperCamelCase__ ) - 1:
																					assert not accelerator.gradient_state.end_of_dataloader
																					if iteration == 1:
																												for batch_num, _ in enumerate(UpperCamelCase__ ):
																																			assert id(accelerator.gradient_state.active_dataloader ) == id(UpperCamelCase__ )
																																			if batch_num < len(UpperCamelCase__ ) - 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_      (   ):


							'''simple docstring'''







							__lowerCamelCase   :   List[str]       =Accelerator()
							__lowerCamelCase   :   Tuple       =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(UpperCamelCase__ )
							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(UpperCamelCase__ )
							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(UpperCamelCase__     ,    UpperCamelCase__ )

    # 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(UpperCamelCase__     ,    UpperCamelCase__ )



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Union[str, Any] ):


							'''simple docstring'''







							main()


if __name__ == "__main__":
							main()











def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				float       , UpperCamelCase__:				float     ):
		if mass < 0:
				raise ValueError("""The mass of a body cannot be negative"""     )
		return 0.5 * mass * abs(UpperCamelCase__     ) * abs(UpperCamelCase__     )


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)