






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     (       a_							,a_							,a_							,a_					)     ->						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     (       a_							,a_							,a_							,a_							,a_=True					)     ->						Tuple:
					model.train()
					__UpperCamelCase      :					List[Any]       		=model(a_					)
					__UpperCamelCase      :					List[Any]       		=F.mse_loss(a_							,target.to(output.device					)					)
					if not do_backward:
										loss /= accelerator.gradient_accumulation_steps
										loss.backward()
					else:
										accelerator.backward(a_					)






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






def 							A     (       a_					)     ->						Union[str, Any]:
					# Test when on a single CPU or GPU that the context manager does nothing
					__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase      :					Any       		=get_training_setup(a_					)
					# Use a single batch
					__UpperCamelCase		,				__UpperCamelCase      :					int       		=next(iter(a_					)					).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      :					Union[str, Any]       		=input.to(accelerator.device					), target.to(accelerator.device					)
										# Perform our initial ground truth step in non "DDP"
										step_model(a_							,a_							,a_							,a_					)
										# Do "gradient accumulation" (noop)
										if iteration % 2 == 0:
															# Accumulate grads locally
															with accelerator.no_sync(a_					):
																				step_model(a_							,a_							,a_							,a_					)
										else:
															# Sync grads
															step_model(a_							,a_							,a_							,a_					)

										# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
										check_model_parameters(a_							,a_							,a_							,a_					)
										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      :					str       		=ddp_input[torch.randperm(len(a_					)					)]






def 							A     (       a_					)     ->						Tuple:
					# Test on distributed setup that context manager behaves properly
					__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase      :					Dict       		=get_training_setup(a_					)
					# Use a single batch
					__UpperCamelCase		,				__UpperCamelCase      :					Optional[int]       		=next(iter(a_					)					).values()
					for iteration in range(3					):
										# 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(a_							,a_							,a_							,a_					)
										# Do "gradient accumulation" (noop)
										if iteration % 2 == 0:
															# Accumulate grads locally
															with accelerator.no_sync(a_					):
																				step_model(a_							,a_							,a_							,a_					)
										else:
															# Sync grads
															step_model(a_							,a_							,a_							,a_					)

										# 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      :					Optional[int]       		=ddp_input[torch.randperm(len(a_					)					)]






def 							A     (       a_=False							,a_=False					)     ->						Tuple:
					__UpperCamelCase      :					Union[str, Any]       		=Accelerator(
					    split_batches=a_							,dispatch_batches=a_							,gradient_accumulation_steps=2					)
					# Test that context manager behaves properly
					__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase      :					Tuple       		=get_training_setup(a_					)
					for iteration, batch in enumerate(a_					):
										__UpperCamelCase		,				__UpperCamelCase      :					Tuple       		=batch.values()
										# Gather the distributed inputs and targs for the base model
										__UpperCamelCase		,				__UpperCamelCase      :					Union[str, Any]       		=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(a_							,a_							,a_							,a_							,a_					)
										# Do "gradient accumulation" (noop)
										with accelerator.accumulate(a_					):
															step_model(a_							,a_							,a_							,a_					)

										# 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(a_					) - 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      :					Union[str, Any]       		=ddp_input[torch.randperm(len(a_					)					)]
					GradientState._reset_state()






def 							A     (       a_=False							,a_=False					)     ->						Dict:
					__UpperCamelCase      :					int       		=Accelerator(
					    split_batches=a_							,dispatch_batches=a_							,gradient_accumulation_steps=2					)
					# Test that context manager behaves properly
					__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase      :					str       		=get_training_setup(a_							,a_					)
					for iteration, batch in enumerate(a_					):
										__UpperCamelCase		,				__UpperCamelCase      :					List[Any]       		=batch.values()
										# Gather the distributed inputs and targs for the base model
										__UpperCamelCase		,				__UpperCamelCase      :					Dict       		=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(a_							,a_							,a_							,a_							,a_					)
										opt.step()

										if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(a_					)):
															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(a_					):
															step_model(a_							,a_							,a_							,a_					)
															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(a_					))
										if accelerator.num_processes > 1:
															check_model_parameters(a_							,a_							,a_							,a_					)
										# Shuffle ddp_input on each iteration
										torch.manual_seed(1_337 + iteration					)
					GradientState._reset_state()






def 							A     (       )     ->						int:
					__UpperCamelCase      :					List[Any]       		=Accelerator()

					__UpperCamelCase      :					Any       		=RegressionDataset(length=80					)
					__UpperCamelCase      :					Any       		=DataLoader(a_							,batch_size=16					)
					__UpperCamelCase      :					List[Any]       		=RegressionDataset(length=96					)
					__UpperCamelCase      :					Optional[int]       		=DataLoader(a_							,batch_size=16					)
					__UpperCamelCase		,				__UpperCamelCase      :					Dict       		=accelerator.prepare(a_							,a_					)
					assert accelerator.gradient_state.active_dataloader is None
					for iteration, _ in enumerate(a_					):
										assert id(accelerator.gradient_state.active_dataloader					) == id(a_					)
										if iteration < len(a_					) - 1:
															assert not accelerator.gradient_state.end_of_dataloader
															if iteration == 1:
																				for batch_num, _ in enumerate(a_					):
																									assert id(accelerator.gradient_state.active_dataloader					) == id(a_					)
																									if batch_num < len(a_					) - 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     (       )     ->						Union[str, Any]:
					__UpperCamelCase      :					List[Any]       		=Accelerator()
					__UpperCamelCase      :					List[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(a_					)
					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(a_					)
					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(a_							,a_					)

    # 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(a_							,a_					)






def 							A     (       a_					)     ->						Dict:
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
							main()




from collections.abc import Callable

class 				UpperCAmelCase_		:
							"""simple docstring"""







							def __init__(			self       ,							_a = None       )    ->    None:
												# Stores actual heap items.
												_a	:    list         =		[]
												# Stores indexes of each item for supporting updates and deletion.
												_a	:    dict         =		{}
												# Stores current size of heap.
												_a	:    Tuple         =		0
												# Stores function used to evaluate the score of an item on which basis ordering
												# will be done.
												_a	:    Dict         =		key or (lambda _a       : x)


							def 				__lowercase				(			self       ,							_a       )    ->    int | None:
												return int((i - 1) / 2       ) if i > 0 else None


							def 				__lowercase				(			self       ,							_a       )    ->    int | None:
												_a	:    Optional[int]         =		int(2 * i + 1       )
												return left if 0 < left < self.size else None


							def 				__lowercase				(			self       ,							_a       )    ->    int | None:
												_a	:    int         =		int(2 * i + 2       )
												return right if 0 < right < self.size else None


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    None:
												_a      ,					_a	:    Union[str, Any]         =		(
												    self.pos_map[self.arr[j][0]],
												    self.pos_map[self.arr[i][0]],
												)
												# Then swap the items in the list.
												_a      ,					_a	:    List[Any]         =		self.arr[j], self.arr[i]


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    bool:
												return self.arr[i][1] < self.arr[j][1]


							def 				__lowercase				(			self       ,							_a       )    ->    int:
												_a	:    Dict         =		self._left(_a       )
												_a	:    str         =		self._right(_a       )
												_a	:    str         =		i

												if left is not None and not self._cmp(_a       ,							_a       ):
																	_a	:    Optional[Any]         =		left
												if right is not None and not self._cmp(_a       ,							_a       ):
																	_a	:    Any         =		right

												return valid_parent


							def 				__lowercase				(			self       ,							_a       )    ->    None:
												_a	:    List[str]         =		self._parent(_a       )
												while parent is not None and not self._cmp(_a       ,							_a       ):
																	self._swap(_a       ,							_a       )
																	_a      ,					_a	:    Any         =		parent, self._parent(_a       )


							def 				__lowercase				(			self       ,							_a       )    ->    None:
												_a	:    List[Any]         =		self._get_valid_parent(_a       )
												while valid_parent != index:
																	self._swap(_a       ,							_a       )
																	_a      ,					_a	:    int         =		valid_parent, self._get_valid_parent(_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    None:
												if item not in self.pos_map:
																	return
												_a	:    str         =		self.pos_map[item]
												_a	:    List[Any]         =		[item, self.key(_a       )]
												# Make sure heap is right in both up and down direction.
												# Ideally only one of them will make any change.
												self._heapify_up(_a       )
												self._heapify_down(_a       )


							def 				__lowercase				(			self       ,							_a       )    ->    None:
												if item not in self.pos_map:
																	return
												_a	:    Tuple         =		self.pos_map[item]
												del self.pos_map[item]
												_a	:    Tuple         =		self.arr[self.size - 1]
												_a	:    str         =		index
												self.size -= 1
												# Make sure heap is right in both up and down direction. Ideally only one
												# of them will make any change- so no performance loss in calling both.
												if self.size > index:
																	self._heapify_up(_a       )
																	self._heapify_down(_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    None:
												_a	:    Union[str, Any]         =		len(self.arr       )
												if arr_len == self.size:
																	self.arr.append([item, self.key(_a       )]       )
												else:
																	_a	:    Optional[int]         =		[item, self.key(_a       )]
												_a	:    Tuple         =		self.size
												self.size += 1
												self._heapify_up(self.size - 1       )


							def 				__lowercase				(			self       )    ->    tuple | None:
												return self.arr[0] if self.size else None




							def 				__lowercase				(			self       )    ->    tuple | None:
												_a	:    Tuple         =		self.get_top()
												if top_item_tuple:
																	self.delete_item(top_item_tuple[0]       )
												return top_item_tuple



def    __UpperCAmelCase (	)			->     None:

					"""simple docstring"""



if __name__ == "__main__":
			import doctest

			doctest.testmod()



