






'''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       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Optional[int]:
		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       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_=True)							->     Dict:
		model.train()
		UpperCamelCase__				:					Any					=					model(lowerCamelCase_)
		UpperCamelCase__				:					Union[str, Any]					=					F.mse_loss(lowerCamelCase_     ,      target.to(output.device))
		if not do_backward:
				loss /= accelerator.gradient_accumulation_steps
				loss.backward()
		else:
				accelerator.backward(lowerCamelCase_)







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







def       __UpperCAmelCase						(  lowerCamelCase_)							->     List[str]:
		# Test when on a single CPU or GPU that the context manager does nothing
		UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					Optional[Any]					=					get_training_setup(lowerCamelCase_)
		# Use a single batch
		UpperCamelCase__,	UpperCamelCase__				:					Union[str, Any]					=					next(iter(lowerCamelCase_)).values()
		for iteration in range(3):
				# Gather the distributed inputs and targs for the base model
				UpperCamelCase__,	UpperCamelCase__				:					int					=					accelerator.gather((ddp_input, ddp_target))
				UpperCamelCase__,	UpperCamelCase__				:					Optional[Any]					=					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_337 + iteration)
				UpperCamelCase__				:					Union[str, Any]					=					ddp_input[torch.randperm(len(lowerCamelCase_))]







def       __UpperCAmelCase						(  lowerCamelCase_)							->     str:
		# Test on distributed setup that context manager behaves properly
		UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					int					=					get_training_setup(lowerCamelCase_)
		# Use a single batch
		UpperCamelCase__,	UpperCamelCase__				:					str					=					next(iter(lowerCamelCase_)).values()
		for iteration in range(3):
				# Gather the distributed inputs and targs for the base model
				UpperCamelCase__,	UpperCamelCase__				:					List[str]					=					accelerator.gather((ddp_input, ddp_target))
				UpperCamelCase__,	UpperCamelCase__				:					Any					=					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_337 + iteration)
				UpperCamelCase__				:					Any					=					ddp_input[torch.randperm(len(lowerCamelCase_))]







def       __UpperCAmelCase						(  lowerCamelCase_=False     ,      lowerCamelCase_=False)							->     Dict:
		UpperCamelCase__				:					Optional[Any]					=					Accelerator(
		    split_batches=lowerCamelCase_     ,      dispatch_batches=lowerCamelCase_     ,      gradient_accumulation_steps=2)
		# Test that context manager behaves properly
		UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					Tuple					=					get_training_setup(lowerCamelCase_)
		for iteration, batch in enumerate(lowerCamelCase_):
				UpperCamelCase__,	UpperCamelCase__				:					Union[str, Any]					=					batch.values()
				# Gather the distributed inputs and targs for the base model
				UpperCamelCase__,	UpperCamelCase__				:					Optional[Any]					=					accelerator.gather((ddp_input, ddp_target))
				UpperCamelCase__,	UpperCamelCase__				:					int					=					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_337 + iteration)
				UpperCamelCase__				:					int					=					ddp_input[torch.randperm(len(lowerCamelCase_))]
		GradientState._reset_state()







def       __UpperCAmelCase						(  lowerCamelCase_=False     ,      lowerCamelCase_=False)							->     Optional[int]:
		UpperCamelCase__				:					Dict					=					Accelerator(
		    split_batches=lowerCamelCase_     ,      dispatch_batches=lowerCamelCase_     ,      gradient_accumulation_steps=2)
		# Test that context manager behaves properly
		UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					List[str]					=					get_training_setup(lowerCamelCase_     ,      lowerCamelCase_)
		for iteration, batch in enumerate(lowerCamelCase_):
				UpperCamelCase__,	UpperCamelCase__				:					int					=					batch.values()
				# Gather the distributed inputs and targs for the base model
				UpperCamelCase__,	UpperCamelCase__				:					List[str]					=					accelerator.gather((ddp_input, ddp_target))
				UpperCamelCase__,	UpperCamelCase__				:					Optional[int]					=					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'
				UpperCamelCase__				:					Tuple					=					(((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_337 + iteration)
		GradientState._reset_state()







def       __UpperCAmelCase						(  )							->     Optional[Any]:
		UpperCamelCase__				:					List[Any]					=					Accelerator()

		UpperCamelCase__				:					Dict					=					RegressionDataset(length=80)
		UpperCamelCase__				:					List[Any]					=					DataLoader(lowerCamelCase_     ,      batch_size=16)
		UpperCamelCase__				:					int					=					RegressionDataset(length=96)
		UpperCamelCase__				:					str					=					DataLoader(lowerCamelCase_     ,      batch_size=16)
		UpperCamelCase__,	UpperCamelCase__				:					List[Any]					=					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       __UpperCAmelCase						(  )							->     Optional[int]:
		UpperCamelCase__				:					str					=					Accelerator()
		UpperCamelCase__				:					int					=					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       __UpperCAmelCase						(  lowerCamelCase_)							->     Tuple:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
		main()












'''simple docstring'''




def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     list[str]:
		return [sentence[i : i + ngram_size] for i in range(len(lowerCamelCase_) - ngram_size + 1)]


if __name__ == "__main__":
		from doctest import testmod

		testmod()




