




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_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				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_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=True ):
				model.train()
				__magic_name__						:						Dict										=model(lowerCamelCase )
				__magic_name__						:						int										=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_ (						lowerCamelCase						,  lowerCamelCase=False ):
				set_seed(42 )
				__magic_name__						:						Optional[int]										=RegressionModel()
				__magic_name__						:						Optional[Any]										=deepcopy(lowerCamelCase )
				__magic_name__						:						List[str]										=RegressionDataset(length=80 )
				__magic_name__						:						Dict										=DataLoader(lowerCamelCase						,  batch_size=16 )
				model.to(accelerator.device )
				if sched:
								__magic_name__						:						Optional[Any]										=AdamW(params=model.parameters()						,  lr=1E-3 )
								__magic_name__						:						Optional[Any]										=AdamW(params=ddp_model.parameters()						,  lr=1E-3 )
								__magic_name__						:						Optional[int]										=LambdaLR(lowerCamelCase						,  lr_lambda=lambda lowerCamelCase : epoch**0.6_5 )
								__magic_name__						:						Dict										=LambdaLR(lowerCamelCase						,  lr_lambda=lambda lowerCamelCase : epoch**0.6_5 )
				# Make a copy of `model`
				if sched:
								__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__						:						Dict										=accelerator.prepare(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
				else:
								__magic_name__					,			__magic_name__						:						int										=accelerator.prepare(lowerCamelCase						,  lowerCamelCase )
				if sched:
								return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
				return model, ddp_model, dataloader




def 		lowerCAmelCase_ (						lowerCamelCase ):
				# Test when on a single CPU or GPU that the context manager does nothing
				__magic_name__					,			__magic_name__					,			__magic_name__						:						Dict										=get_training_setup(lowerCamelCase )
				# Use a single batch
				__magic_name__					,			__magic_name__						:						Optional[Any]										=next(iter(lowerCamelCase ) ).values()
				for iteration in range(3 ):
								# Gather the distributed inputs and targs for the base model
								__magic_name__					,			__magic_name__						:						Any										=accelerator.gather((ddp_input, ddp_target) )
								__magic_name__					,			__magic_name__						:						str										=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 )
								__magic_name__						:						Dict										=ddp_input[torch.randperm(len(lowerCamelCase ) )]




def 		lowerCAmelCase_ (						lowerCamelCase ):
				# Test on distributed setup that context manager behaves properly
				__magic_name__					,			__magic_name__					,			__magic_name__						:						Dict										=get_training_setup(lowerCamelCase )
				# Use a single batch
				__magic_name__					,			__magic_name__						:						Any										=next(iter(lowerCamelCase ) ).values()
				for iteration in range(3 ):
								# Gather the distributed inputs and targs for the base model
								__magic_name__					,			__magic_name__						:						Tuple										=accelerator.gather((ddp_input, ddp_target) )
								__magic_name__					,			__magic_name__						:						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(1337 + iteration )
								__magic_name__						:						Optional[Any]										=ddp_input[torch.randperm(len(lowerCamelCase ) )]




def 		lowerCAmelCase_ (						lowerCamelCase=False						,  lowerCamelCase=False ):
				__magic_name__						:						List[Any]										=Accelerator(
				    split_batches=lowerCamelCase						,  dispatch_batches=lowerCamelCase						,  gradient_accumulation_steps=2 )
				# Test that context manager behaves properly
				__magic_name__					,			__magic_name__					,			__magic_name__						:						str										=get_training_setup(lowerCamelCase )
				for iteration, batch in enumerate(lowerCamelCase ):
								__magic_name__					,			__magic_name__						:						List[Any]										=batch.values()
								# Gather the distributed inputs and targs for the base model
								__magic_name__					,			__magic_name__						:						Optional[Any]										=accelerator.gather((ddp_input, ddp_target) )
								__magic_name__					,			__magic_name__						:						str										=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 )
								__magic_name__						:						List[Any]										=ddp_input[torch.randperm(len(lowerCamelCase ) )]
				GradientState._reset_state()




def 		lowerCAmelCase_ (						lowerCamelCase=False						,  lowerCamelCase=False ):
				__magic_name__						:						Union[str, Any]										=Accelerator(
				    split_batches=lowerCamelCase						,  dispatch_batches=lowerCamelCase						,  gradient_accumulation_steps=2 )
				# Test that context manager behaves properly
				__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__						:						Any										=get_training_setup(lowerCamelCase						,  lowerCamelCase )
				for iteration, batch in enumerate(lowerCamelCase ):
								__magic_name__					,			__magic_name__						:						str										=batch.values()
								# Gather the distributed inputs and targs for the base model
								__magic_name__					,			__magic_name__						:						List[Any]										=accelerator.gather((ddp_input, ddp_target) )
								__magic_name__					,			__magic_name__						:						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(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"
								__magic_name__						:						Union[str, Any]										=(((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_ (						):
				__magic_name__						:						Optional[int]										=Accelerator()

				__magic_name__						:						List[Any]										=RegressionDataset(length=80 )
				__magic_name__						:						Union[str, Any]										=DataLoader(lowerCamelCase						,  batch_size=16 )
				__magic_name__						:						Tuple										=RegressionDataset(length=96 )
				__magic_name__						:						Dict										=DataLoader(lowerCamelCase						,  batch_size=16 )
				__magic_name__					,			__magic_name__						:						Union[str, 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 		lowerCAmelCase_ (						):
				__magic_name__						:						str										=Accelerator()
				__magic_name__						:						str										=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_ (						lowerCamelCase ):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()










from collections.abc import Sequence




def 		lowerCAmelCase_ (						lowerCamelCase = None ):
				if nums is None or not nums:
								raise ValueError("""Input sequence should not be empty""" )

				__magic_name__						:						str										=nums[0]
				for i in range(1						,  len(lowerCamelCase ) ):
								__magic_name__						:						Any										=nums[i]
								__magic_name__						:						Dict										=max(lowerCamelCase						,  ans + num						,  lowerCamelCase )

				return ans


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Try on a sample input from the user
							UpperCAmelCase_		:					List[str]          =  int(input("Enter number of elements : ").strip())
							UpperCAmelCase_		:					Tuple          =  list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
							print(max_subsequence_sum(array))




