


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_					)       ->		Dict:
    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					)       ->		Optional[int]:
    model.train()
    _lowercase			:				List[Any] 						=						model(lowerCamelCase_					)
    _lowercase			:				str 						=						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					)       ->		Tuple:
    set_seed(42					)
    _lowercase			:				List[str] 						=						RegressionModel()
    _lowercase			:				Union[str, Any] 						=						deepcopy(lowerCamelCase_					)
    _lowercase			:				str 						=						RegressionDataset(length=80					)
    _lowercase			:				Dict 						=						DataLoader(lowerCamelCase_				,					batch_size=16					)
    model.to(accelerator.device					)
    if sched:
        _lowercase			:				int 						=						AdamW(params=model.parameters()				,					lr=1e-3					)
        _lowercase			:				Any 						=						AdamW(params=ddp_model.parameters()				,					lr=1e-3					)
        _lowercase			:				Optional[int] 						=						LambdaLR(lowerCamelCase_				,					lr_lambda=lambda lowerCamelCase_					: epoch**0.65					)
        _lowercase			:				int 						=						LambdaLR(lowerCamelCase_				,					lr_lambda=lambda lowerCamelCase_					: epoch**0.65					)
    # Make a copy of `model`
    if sched:
        _lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				int 						=						accelerator.prepare(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    else:
        _lowercase						,							_lowercase			:				str 						=						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_					)       ->		Optional[int]:
    # Test when on a single CPU or GPU that the context manager does nothing
    _lowercase						,							_lowercase						,							_lowercase			:				Optional[int] 						=						get_training_setup(lowerCamelCase_					)
    # Use a single batch
    _lowercase						,							_lowercase			:				int 						=						next(iter(lowerCamelCase_					)					).values()
    for iteration in range(3					):
        # Gather the distributed inputs and targs for the base model
        _lowercase						,							_lowercase			:				Optional[int] 						=						accelerator.gather((ddp_input, ddp_target)					)
        _lowercase						,							_lowercase			:				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(1337 + iteration					)
        _lowercase			:				str 						=						ddp_input[torch.randperm(len(lowerCamelCase_					)					)]



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



def 							UpperCamelCase_( lowerCamelCase_=False				,					lowerCamelCase_=False					)       ->		Optional[Any]:
    _lowercase			:				Dict 						=						Accelerator(
        split_batches=lowerCamelCase_				,					dispatch_batches=lowerCamelCase_				,					gradient_accumulation_steps=2					)
    # Test that context manager behaves properly
    _lowercase						,							_lowercase						,							_lowercase			:				Optional[Any] 						=						get_training_setup(lowerCamelCase_					)
    for iteration, batch in enumerate(lowerCamelCase_					):
        _lowercase						,							_lowercase			:				Any 						=						batch.values()
        # Gather the distributed inputs and targs for the base model
        _lowercase						,							_lowercase			:				int 						=						accelerator.gather((ddp_input, ddp_target)					)
        _lowercase						,							_lowercase			:				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(1337 + iteration					)
        _lowercase			:				List[str] 						=						ddp_input[torch.randperm(len(lowerCamelCase_					)					)]
    GradientState._reset_state()



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

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


if __name__ == "__main__":
 main()









def      UpperCAmelCase_    (    _UpperCAmelCase							:list				)	->				list:
     '''simple docstring'''


     if len(_UpperCAmelCase				) < 2:
          return collection

     def circle_sort_util(_UpperCAmelCase							:list  ,						_UpperCAmelCase							:int  ,						_UpperCAmelCase							:int				) -> bool:
          A_          =       False

          if low == high:
               return swapped

          A_          =       low
          A_          =       high

          while left < right:
               if collection[left] > collection[right]:
                    A_   ,    A_          =       (
                        collection[right],
                        collection[left],
                    )
                    A_          =       True

               left += 1
               right -= 1

          if left == right and collection[left] > collection[right + 1]:
               A_   ,    A_          =       (
                   collection[right + 1],
                   collection[left],
               )

               A_          =       True

          A_          =       low + int((high - low) / 2				)
          A_          =       circle_sort_util(_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase				)
          A_          =       circle_sort_util(_UpperCAmelCase  ,						mid + 1  ,						_UpperCAmelCase				)

          return swapped or left_swap or right_swap

     A_          =       True

     while is_not_sorted is True:
          A_          =       circle_sort_util(_UpperCAmelCase  ,						0  ,						len(_UpperCAmelCase				) - 1				)

     return collection


if __name__ == "__main__":
      a__      :     Optional[Any]         =		input('Enter numbers separated by a comma:\n').strip()
      a__      :     List[str]         =		[int(item) for item in user_input.split(',')]
      print(circle_sort(unsorted))



