"""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__			(	_UpperCamelCase				: Dict       ,	_UpperCamelCase				: Union[str, Any]       ,	_UpperCamelCase				: Optional[Any]       ,	_UpperCamelCase				: List[Any]    )    -> List[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__			(	_UpperCamelCase				: Any       ,	_UpperCamelCase				: Tuple       ,	_UpperCamelCase				: Any       ,	_UpperCamelCase				: List[Any]       ,	_UpperCamelCase				: str=True    )    -> Optional[Any]:





						"""simple docstring"""






						model.train()
						snake_case	    =	model(snake_case_    )
						snake_case	    =	F.mse_loss(snake_case_       ,	target.to(output.device    )    )
						if not do_backward:
												loss /= accelerator.gradient_accumulation_steps
												loss.backward()
						else:
												accelerator.backward(snake_case_    )





def       lowerCAmelCase__			(	_UpperCamelCase				: Optional[Any]       ,	_UpperCamelCase				: Any=False    )    -> Dict:





						"""simple docstring"""






						set_seed(4_2    )
						snake_case	    =	RegressionModel()
						snake_case	    =	deepcopy(snake_case_    )
						snake_case	    =	RegressionDataset(length=8_0    )
						snake_case	    =	DataLoader(snake_case_       ,	batch_size=1_6    )
						model.to(accelerator.device    )
						if sched:
												snake_case	    =	AdamW(params=model.parameters()       ,	lr=1e-3    )
												snake_case	    =	AdamW(params=ddp_model.parameters()       ,	lr=1e-3    )
												snake_case	    =	LambdaLR(snake_case_       ,	lr_lambda=lambda _UpperCamelCase    : epoch**0.65    )
												snake_case	    =	LambdaLR(snake_case_       ,	lr_lambda=lambda _UpperCamelCase    : epoch**0.65    )
						# Make a copy of `model`
						if sched:
												snake_case    ,snake_case    ,snake_case    ,snake_case	    =	accelerator.prepare(snake_case_       ,	snake_case_       ,	snake_case_       ,	snake_case_    )
						else:
												snake_case    ,snake_case	    =	accelerator.prepare(snake_case_       ,	snake_case_    )
						if sched:
												return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
						return model, ddp_model, dataloader





def       lowerCAmelCase__			(	_UpperCamelCase				: Any    )    -> int:





						"""simple docstring"""






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

												# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
												check_model_parameters(snake_case_       ,	snake_case_       ,	snake_case_       ,	snake_case_    )
												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(1_3_3_7 + iteration    )
												snake_case	    =	ddp_input[torch.randperm(len(snake_case_    )    )]





def       lowerCAmelCase__			(	_UpperCamelCase				: Tuple    )    -> str:





						"""simple docstring"""






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

												# 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(1_3_3_7 + iteration    )
												snake_case	    =	ddp_input[torch.randperm(len(snake_case_    )    )]





def       lowerCAmelCase__			(	_UpperCamelCase				: Optional[int]=False       ,	_UpperCamelCase				: str=False    )    -> List[str]:





						"""simple docstring"""






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

												# 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(snake_case_    ) - 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(1_3_3_7 + iteration    )
												snake_case	    =	ddp_input[torch.randperm(len(snake_case_    )    )]
						GradientState._reset_state()





def       lowerCAmelCase__			(	_UpperCamelCase				: Optional[Any]=False       ,	_UpperCamelCase				: Tuple=False    )    -> Union[str, Any]:





						"""simple docstring"""






						snake_case	    =	Accelerator(
						    split_batches=snake_case_       ,	dispatch_batches=snake_case_       ,	gradient_accumulation_steps=2    )
						# Test that context manager behaves properly
						snake_case    ,snake_case    ,snake_case    ,snake_case    ,snake_case    ,snake_case    ,snake_case	    =	get_training_setup(snake_case_       ,	snake_case_    )
						for iteration, batch in enumerate(snake_case_    ):
												snake_case    ,snake_case	    =	batch.values()
												# Gather the distributed inputs and targs for the base model
												snake_case    ,snake_case	    =	accelerator.gather((ddp_input, ddp_target)    )
												snake_case    ,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(snake_case_       ,	snake_case_       ,	snake_case_       ,	snake_case_       ,	snake_case_    )
												opt.step()

												if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(snake_case_    )):
																		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(snake_case_    ):
																		step_model(snake_case_       ,	snake_case_       ,	snake_case_       ,	snake_case_    )
																		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"""
												snake_case	    =	(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(snake_case_    ))
												if accelerator.num_processes > 1:
																		check_model_parameters(snake_case_       ,	snake_case_       ,	snake_case_       ,	snake_case_    )
												# Shuffle ddp_input on each iteration
												torch.manual_seed(1_3_3_7 + iteration    )
						GradientState._reset_state()





def       lowerCAmelCase__			(	)    -> List[Any]:





						"""simple docstring"""






						snake_case	    =	Accelerator()

						snake_case	    =	RegressionDataset(length=8_0    )
						snake_case	    =	DataLoader(snake_case_       ,	batch_size=1_6    )
						snake_case	    =	RegressionDataset(length=9_6    )
						snake_case	    =	DataLoader(snake_case_       ,	batch_size=1_6    )
						snake_case    ,snake_case	    =	accelerator.prepare(snake_case_       ,	snake_case_    )
						assert accelerator.gradient_state.active_dataloader is None
						for iteration, _ in enumerate(snake_case_    ):
												assert id(accelerator.gradient_state.active_dataloader    ) == id(snake_case_    )
												if iteration < len(snake_case_    ) - 1:
																		assert not accelerator.gradient_state.end_of_dataloader
																		if iteration == 1:
																								for batch_num, _ in enumerate(snake_case_    ):
																														assert id(accelerator.gradient_state.active_dataloader    ) == id(snake_case_    )
																														if batch_num < len(snake_case_    ) - 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__			(	)    -> str:





						"""simple docstring"""






						snake_case	    =	Accelerator()
						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(snake_case_    )
						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(snake_case_    )
						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(snake_case_       ,	snake_case_    )

    # 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(snake_case_       ,	snake_case_    )





def       lowerCAmelCase__			(	_UpperCamelCase				: Dict    )    -> int:





						"""simple docstring"""






						main()


if __name__ == "__main__":
					main()



'''simple docstring'''





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : int			)    ->      str:


     '''simple docstring'''




     if a < 0 or b < 0:
          raise ValueError("the value of both inputs must be positive"			)
     UpperCAmelCase_           =   str(bin(snake_case_			)			)[2:]  # remove the leading "0b"
     UpperCAmelCase_           =   str(bin(snake_case_			)			)[2:]
     UpperCAmelCase_           =   max(len(snake_case_			)					,    len(snake_case_			)			)
     return "0b" + "".join(
         str(int("1" in (char_a, char_b)			)			)
         for char_a, char_b in zip(a_binary.zfill(snake_case_			)					,    b_binary.zfill(snake_case_			)			)			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()




