


"""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   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[str]:
							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   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=True			)      ->			Tuple:
							model.train()
							snake_case_          =						model(__lowerCamelCase			)
							snake_case_          =						F.mse_loss(__lowerCamelCase			,	target.to(output.device			)			)
							if not do_backward:
														loss /= accelerator.gradient_accumulation_steps
														loss.backward()
							else:
														accelerator.backward(__lowerCamelCase			)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=False			)      ->			int:
							set_seed(42			)
							snake_case_          =						RegressionModel()
							snake_case_          =						deepcopy(__lowerCamelCase			)
							snake_case_          =						RegressionDataset(length=80			)
							snake_case_          =						DataLoader(__lowerCamelCase			,	batch_size=16			)
							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(__lowerCamelCase			,	lr_lambda=lambda _SCREAMING_SNAKE_CASE			: epoch**0.65			)
														snake_case_          =						LambdaLR(__lowerCamelCase			,	lr_lambda=lambda _SCREAMING_SNAKE_CASE			: epoch**0.65			)
							# Make a copy of `model`
							if sched:
														snake_case_          =						accelerator.prepare(__lowerCamelCase			,	__lowerCamelCase			,	__lowerCamelCase			,	__lowerCamelCase			)
							else:
														snake_case_          =						accelerator.prepare(__lowerCamelCase			,	__lowerCamelCase			)
							if sched:
														return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
							return model, ddp_model, dataloader







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Tuple:
							# Test when on a single CPU or GPU that the context manager does nothing
							snake_case_          =						get_training_setup(__lowerCamelCase			)
							# Use a single batch
							snake_case_          =						next(iter(__lowerCamelCase			)			).values()
							for iteration in range(3			):
														# Gather the distributed inputs and targs for the base model
														snake_case_          =						accelerator.gather((ddp_input, ddp_target)			)
														snake_case_          =						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			)
														snake_case_          =						ddp_input[torch.randperm(len(__lowerCamelCase			)			)]







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







def   _a			( _SCREAMING_SNAKE_CASE=False			,	_SCREAMING_SNAKE_CASE=False			)      ->			Tuple:
							snake_case_          =						Accelerator(
							    split_batches=__lowerCamelCase			,	dispatch_batches=__lowerCamelCase			,	gradient_accumulation_steps=2			)
							# Test that context manager behaves properly
							snake_case_          =						get_training_setup(__lowerCamelCase			)
							for iteration, batch in enumerate(__lowerCamelCase			):
														snake_case_          =						batch.values()
														# Gather the distributed inputs and targs for the base model
														snake_case_          =						accelerator.gather((ddp_input, ddp_target)			)
														snake_case_          =						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			)
														snake_case_          =						ddp_input[torch.randperm(len(__lowerCamelCase			)			)]
							GradientState._reset_state()







def   _a			( _SCREAMING_SNAKE_CASE=False			,	_SCREAMING_SNAKE_CASE=False			)      ->			List[str]:
							snake_case_          =						Accelerator(
							    split_batches=__lowerCamelCase			,	dispatch_batches=__lowerCamelCase			,	gradient_accumulation_steps=2			)
							# Test that context manager behaves properly
							snake_case_          =						get_training_setup(__lowerCamelCase			,	__lowerCamelCase			)
							for iteration, batch in enumerate(__lowerCamelCase			):
														snake_case_          =						batch.values()
														# Gather the distributed inputs and targs for the base model
														snake_case_          =						accelerator.gather((ddp_input, ddp_target)			)
														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(__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"""
														snake_case_          =						(((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   _a			( )      ->			int:
							snake_case_          =						Accelerator()

							snake_case_          =						RegressionDataset(length=80			)
							snake_case_          =						DataLoader(__lowerCamelCase			,	batch_size=16			)
							snake_case_          =						RegressionDataset(length=96			)
							snake_case_          =						DataLoader(__lowerCamelCase			,	batch_size=16			)
							snake_case_          =						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   _a			( )      ->			List[Any]:
							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(__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   _a			( _SCREAMING_SNAKE_CASE			)      ->			str:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
				main()







"""simple docstring"""
def   _a			( _SCREAMING_SNAKE_CASE			)      ->			list[int]:
							snake_case_          =						len(_SCREAMING_SNAKE_CASE			)
							for i in range(_SCREAMING_SNAKE_CASE			):
														for j in range(i + 1			,	_SCREAMING_SNAKE_CASE			):
																					if numbers[j] < numbers[i]:
																												snake_case_    ,  snake_case_          =						numbers[j], numbers[i]
							return numbers


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



