





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 (							__a       :Optional[Any]   ,					__a       :List[Any]   ,					__a       :List[Any]   ,					__a       :List[Any]   )		->    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 (							__a       :List[Any]   ,					__a       :Optional[int]   ,					__a       :Optional[Any]   ,					__a       :Optional[int]   ,					__a       :Optional[int]=True   )		->    Union[str, Any]:






		"""simple docstring"""

		model.train()
		A__  				=       model(lowerCamelCase_   )
		A__  				=       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 (							__a       :Optional[Any]   ,					__a       :Any=False   )		->    Optional[int]:






		"""simple docstring"""

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







def        __lowerCamelCase (							__a       :Optional[Any]   )		->    List[Any]:






		"""simple docstring"""

		A__  				=       get_training_setup(lowerCamelCase_   )
		# Use a single batch
		A__  				=       next(iter(lowerCamelCase_   )   ).values()
		for iteration in range(3   ):
				# Gather the distributed inputs and targs for the base model
				A__  				=       accelerator.gather((ddp_input, ddp_target)   )
				A__  				=       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(1_3_3_7 + iteration   )
				A__  				=       ddp_input[torch.randperm(len(lowerCamelCase_   )   )]







def        __lowerCamelCase (							__a       :Optional[int]   )		->    Union[str, Any]:






		"""simple docstring"""

		A__  				=       get_training_setup(lowerCamelCase_   )
		# Use a single batch
		A__  				=       next(iter(lowerCamelCase_   )   ).values()
		for iteration in range(3   ):
				# Gather the distributed inputs and targs for the base model
				A__  				=       accelerator.gather((ddp_input, ddp_target)   )
				A__  				=       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(1_3_3_7 + iteration   )
				A__  				=       ddp_input[torch.randperm(len(lowerCamelCase_   )   )]







def        __lowerCamelCase (							__a       :str=False   ,					__a       :Optional[Any]=False   )		->    Dict:






		"""simple docstring"""

		A__  				=       Accelerator(
		    split_batches=lowerCamelCase_   ,					dispatch_batches=lowerCamelCase_   ,					gradient_accumulation_steps=2   )
		# Test that context manager behaves properly
		A__  				=       get_training_setup(lowerCamelCase_   )
		for iteration, batch in enumerate(lowerCamelCase_   ):
				A__  				=       batch.values()
				# Gather the distributed inputs and targs for the base model
				A__  				=       accelerator.gather((ddp_input, ddp_target)   )
				A__  				=       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(1_3_3_7 + iteration   )
				A__  				=       ddp_input[torch.randperm(len(lowerCamelCase_   )   )]
		GradientState._reset_state()







def        __lowerCamelCase (							__a       :Tuple=False   ,					__a       :Tuple=False   )		->    Union[str, Any]:






		"""simple docstring"""

		A__  				=       Accelerator(
		    split_batches=lowerCamelCase_   ,					dispatch_batches=lowerCamelCase_   ,					gradient_accumulation_steps=2   )
		# Test that context manager behaves properly
		A__  				=       get_training_setup(lowerCamelCase_   ,					lowerCamelCase_   )
		for iteration, batch in enumerate(lowerCamelCase_   ):
				A__  				=       batch.values()
				# Gather the distributed inputs and targs for the base model
				A__  				=       accelerator.gather((ddp_input, ddp_target)   )
				A__  				=       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'
				A__  				=       (((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(1_3_3_7 + iteration   )
		GradientState._reset_state()







def        __lowerCamelCase (							)		->    List[str]:






		"""simple docstring"""

		A__  				=       Accelerator()

		A__  				=       RegressionDataset(length=8_0   )
		A__  				=       DataLoader(lowerCamelCase_   ,					batch_size=1_6   )
		A__  				=       RegressionDataset(length=9_6   )
		A__  				=       DataLoader(lowerCamelCase_   ,					batch_size=1_6   )
		A__  				=       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 (							)		->    List[Any]:






		"""simple docstring"""

		A__  				=       Accelerator()
		A__  				=       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 (							__a       :str   )		->    Tuple:






		"""simple docstring"""

		main()


if __name__ == "__main__":
					main()








from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors







def        __lowerCamelCase (							__a       :int   )		->    int:






		"""simple docstring"""

		A__  				=       prime_factors(__a   )
		if is_square_free(__a   ):
				return -1 if len(__a   ) % 2 else 1
		return 0


if __name__ == "__main__":
					import doctest

					doctest.testmod()



