



"""simple docstring"""

from __future__ import annotations

from random import random
from typing import Generic, TypeVar

lowercase__       =	TypeVar("KT")
lowercase__       =	TypeVar("VT")




class SCREAMING_SNAKE_CASE__       (		Generic[KT, VT]   ):



						def __init__(self    ,  _lowercase = "root"    ,  _lowercase = None				):





													'''simple docstring'''

													__a		:		Any						=							key
													__a		:		Tuple						=							value
													__a		:		List[Any]						=							[]



						def __repr__(self				):





													'''simple docstring'''

													return F'''Node({self.key}: {self.value})'''





						@property
						def      lowerCAmelCase__(self				):





													'''simple docstring'''

													return len(self.forward				)







class SCREAMING_SNAKE_CASE__       (		Generic[KT, VT]   ):



						def __init__(self    ,  _lowercase = 0.5    ,  _lowercase = 16				):





													'''simple docstring'''

													__a		:		int						=							Node[KT, VT]()
													__a		:		int						=							0
													__a		:		List[str]						=							p
													__a		:		Union[str, Any]						=							max_level



						def __str__(self				):





													'''simple docstring'''

													__a		:		int						=							list(self				)

													if len(__A				) == 0:
																				return F'''SkipList(level={self.level})'''

													__a		:		Tuple						=							max((len(str(__A				)				) for item in items)    ,  default=4				)
													__a		:		Any						=							max(__A    ,  4				) + 4

													__a		:		Optional[int]						=							self.head
													__a		:		Any						=							[]

													__a		:		Any						=							node.forward.copy()
													lines.append(F'''[{node.key}]'''.ljust(__A    ,  """-"""				) + """* """ * len(__A				)				)
													lines.append(""" """ * label_size + """| """ * len(__A				)				)

													while len(node.forward				) != 0:
																				__a		:		Dict						=							node.forward[0]

																				lines.append(
																				    F'''[{node.key}]'''.ljust(__A    ,  """-"""				)
																				    + """ """.join(str(n.key				) if n.key == node.key else """|""" for n in forwards				)				)
																				lines.append(""" """ * label_size + """| """ * len(__A				)				)
																				__a		:		int						=							node.forward

													lines.append("""None""".ljust(__A				) + """* """ * len(__A				)				)
													return F'''SkipList(level={self.level})\n''' + "\n".join(__A				)



						def __iter__(self				):





													'''simple docstring'''

													__a		:		Tuple						=							self.head

													while len(node.forward				) != 0:
																				yield node.forward[0].key
																				__a		:		List[str]						=							node.forward[0]



						def      lowerCAmelCase__(self				):





													'''simple docstring'''

													__a		:		List[Any]						=							1
													while random() < self.p and level < self.max_level:
																				level += 1

													return level



						def      lowerCAmelCase__(self    ,  _lowercase				):





													'''simple docstring'''

													__a		:		Dict						=							[]

													__a		:		Any						=							self.head

													for i in reversed(range(self.level				)				):
																				# i < node.level - When node level is lesser than `i` decrement `i`.
																				# node.forward[i].key < key - Jumping to node with key value higher
																				#                             or equal to searched key would result
																				#                             in skipping searched key.
																				while i < node.level and node.forward[i].key < key:
																											__a		:		Dict						=							node.forward[i]
																				# Each leftmost node (relative to searched node) will potentially have to
																				# be updated.
																				update_vector.append(__A				)

													update_vector.reverse()  # Note that we were inserting values in reverse order.

													# len(node.forward) != 0 - If current node doesn't contain any further
													#                          references then searched key is not present.
													# node.forward[0].key == key - Next node key should be equal to search key
													#                              if key is present.
													if len(node.forward				) != 0 and node.forward[0].key == key:
																				return node.forward[0], update_vector
													else:
																				return None, update_vector



						def      lowerCAmelCase__(self    ,  _lowercase				):





													'''simple docstring'''

													__a				,  __a		:		Tuple						=							self._locate_node(__A				)

													if node is not None:
																				for i, update_node in enumerate(__A				):
																											# Remove or replace all references to removed node.
																											if update_node.level > i and update_node.forward[i].key == key:
																																		if node.level > i:
																																									__a		:		Dict						=							node.forward[i]
																																		else:
																																									__a		:		List[str]						=							update_node.forward[:i]



						def      lowerCAmelCase__(self    ,  _lowercase    ,  _lowercase				):





													'''simple docstring'''

													__a				,  __a		:		Dict						=							self._locate_node(__A				)
													if node is not None:
																				__a		:		str						=							value
													else:
																				__a		:		Any						=							self.random_level()

																				if level > self.level:
																											# After level increase we have to add additional nodes to head.
																											for _ in range(self.level - 1    ,  __A				):
																																		update_vector.append(self.head				)
																											__a		:		Any						=							level

																				__a		:		int						=							Node(__A    ,  __A				)

																				for i, update_node in enumerate(update_vector[:level]				):
																											# Change references to pass through new node.
																											if update_node.level > i:
																																		new_node.forward.append(update_node.forward[i]				)

																											if update_node.level < i + 1:
																																		update_node.forward.append(__A				)
																											else:
																																		__a		:		Union[str, Any]						=							new_node





						def      lowerCAmelCase__(self    ,  _lowercase				):





													'''simple docstring'''

													__a				,  __a		:		str						=							self._locate_node(__A				)

													if node is not None:
																				return node.value

													return None







def    __magic_name__     ( ):
							__a		:		Any						=							SkipList()
							skip_list.insert("""Key1"""	,							3       )
							skip_list.insert("""Key2"""	,							1_2       )
							skip_list.insert("""Key3"""	,							4_1       )
							skip_list.insert("""Key4"""	,							-1_9       )

							__a		:		Any						=							skip_list.head
							__a		:		Tuple						=							{}
							while node.level != 0:
														__a		:		List[str]						=							node.forward[0]
														__a		:		Optional[int]						=							node.value

							assert len(UpperCamelCase__       ) == 4
							assert all_values["Key1"] == 3
							assert all_values["Key2"] == 1_2
							assert all_values["Key3"] == 4_1
							assert all_values["Key4"] == -1_9





def    __magic_name__     ( ):
							__a		:		List[str]						=							SkipList()
							skip_list.insert("""Key1"""	,							1_0       )
							skip_list.insert("""Key1"""	,							1_2       )

							skip_list.insert("""Key5"""	,							7       )
							skip_list.insert("""Key7"""	,							1_0       )
							skip_list.insert("""Key10"""	,							5       )

							skip_list.insert("""Key7"""	,							7       )
							skip_list.insert("""Key5"""	,							5       )
							skip_list.insert("""Key10"""	,							1_0       )

							__a		:		Tuple						=							skip_list.head
							__a		:		Any						=							{}
							while node.level != 0:
														__a		:		Any						=							node.forward[0]
														__a		:		Tuple						=							node.value

							if len(UpperCamelCase__       ) != 4:
														print()
							assert len(UpperCamelCase__       ) == 4
							assert all_values["Key1"] == 1_2
							assert all_values["Key7"] == 7
							assert all_values["Key5"] == 5
							assert all_values["Key10"] == 1_0





def    __magic_name__     ( ):
							__a		:		Any						=							SkipList()
							assert skip_list.find("""Some key"""       ) is None





def    __magic_name__     ( ):
							__a		:		Any						=							SkipList()

							skip_list.insert("""Key2"""	,							2_0       )
							assert skip_list.find("""Key2"""       ) == 2_0

							skip_list.insert("""Some Key"""	,							1_0       )
							skip_list.insert("""Key2"""	,							8       )
							skip_list.insert("""V"""	,							1_3       )

							assert skip_list.find("""Y"""       ) is None
							assert skip_list.find("""Key2"""       ) == 8
							assert skip_list.find("""Some Key"""       ) == 1_0
							assert skip_list.find("""V"""       ) == 1_3





def    __magic_name__     ( ):
							__a		:		Optional[int]						=							SkipList()
							skip_list.delete("""Some key"""       )

							assert len(skip_list.head.forward       ) == 0





def    __magic_name__     ( ):
							__a		:		List[str]						=							SkipList()

							skip_list.insert("""Key1"""	,							1_2       )
							skip_list.insert("""V"""	,							1_3       )
							skip_list.insert("""X"""	,							1_4       )
							skip_list.insert("""Key2"""	,							1_5       )

							skip_list.delete("""V"""       )
							skip_list.delete("""Key2"""       )

							assert skip_list.find("""V"""       ) is None
							assert skip_list.find("""Key2"""       ) is None





def    __magic_name__     ( ):
							__a		:		List[Any]						=							SkipList()

							skip_list.insert("""Key1"""	,							1_2       )
							skip_list.insert("""V"""	,							1_3       )
							skip_list.insert("""X"""	,							1_4       )
							skip_list.insert("""Key2"""	,							1_5       )

							skip_list.delete("""V"""       )
							assert skip_list.find("""V"""       ) is None
							assert skip_list.find("""X"""       ) == 1_4
							assert skip_list.find("""Key1"""       ) == 1_2
							assert skip_list.find("""Key2"""       ) == 1_5

							skip_list.delete("""X"""       )
							assert skip_list.find("""V"""       ) is None
							assert skip_list.find("""X"""       ) is None
							assert skip_list.find("""Key1"""       ) == 1_2
							assert skip_list.find("""Key2"""       ) == 1_5

							skip_list.delete("""Key1"""       )
							assert skip_list.find("""V"""       ) is None
							assert skip_list.find("""X"""       ) is None
							assert skip_list.find("""Key1"""       ) is None
							assert skip_list.find("""Key2"""       ) == 1_5

							skip_list.delete("""Key2"""       )
							assert skip_list.find("""V"""       ) is None
							assert skip_list.find("""X"""       ) is None
							assert skip_list.find("""Key1"""       ) is None
							assert skip_list.find("""Key2"""       ) is None





def    __magic_name__     ( ):
							__a		:		Optional[Any]						=							SkipList()

							skip_list.insert("""Key1"""	,							1_2       )
							skip_list.insert("""V"""	,							1_3       )
							skip_list.insert("""X"""	,							1_4_2       )
							skip_list.insert("""Key2"""	,							1_5       )

							skip_list.delete("""X"""       )

							def traverse_keys(_lowerCamelCase					:			List[Any]       ):
														yield node.key
														for forward_node in node.forward:
																					yield from traverse_keys(UpperCamelCase__       )

							assert len(set(traverse_keys(skip_list.head       )       )       ) == 4





def    __magic_name__     ( ):
							def is_sorted(_lowerCamelCase					:			str       ):
														return all(next_item >= item for item, next_item in zip(UpperCamelCase__	,							lst[1:]       )       )

							__a		:		List[str]						=							SkipList()
							for i in range(1_0       ):
														skip_list.insert(UpperCamelCase__	,							UpperCamelCase__       )
							assert is_sorted(list(UpperCamelCase__       )       )
							skip_list.delete(5       )
							skip_list.delete(8       )
							skip_list.delete(2       )
							assert is_sorted(list(UpperCamelCase__       )       )
							skip_list.insert(-1_2	,							-1_2       )
							skip_list.insert(7_7	,							7_7       )
							assert is_sorted(list(UpperCamelCase__       )       )





def    __magic_name__     ( ):
							for _ in range(1_0_0       ):
														# Repeat test 100 times due to the probabilistic nature of skip list
														# random values == random bugs
														test_insert()
														test_insert_overrides_existing_value()

														test_searching_empty_list_returns_none()
														test_search()

														test_deleting_item_from_empty_list_do_nothing()
														test_deleted_items_are_not_founded_by_find_method()
														test_delete_removes_only_given_key()
														test_delete_doesnt_leave_dead_nodes()

														test_iter_always_yields_sorted_values()





def    __magic_name__     ( ):
							__a		:		Any						=							SkipList()
							skip_list.insert(2	,							"""2"""       )
							skip_list.insert(4	,							"""4"""       )
							skip_list.insert(6	,							"""4"""       )
							skip_list.insert(4	,							"""5"""       )
							skip_list.insert(8	,							"""4"""       )
							skip_list.insert(9	,							"""4"""       )

							skip_list.delete(4       )

							print(UpperCamelCase__       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()








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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]       , UpperCamelCase__:				str       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Union[str, Any]     ):
		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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Any       , UpperCamelCase__:				List[str]       , UpperCamelCase__:				Tuple=True     ):
		model.train()
		SCREAMING_SNAKE_CASE__								=   model(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   F.mse_loss(UpperCamelCase__       , target.to(output.device     )     )
		if not do_backward:
				loss /= accelerator.gradient_accumulation_steps
				loss.backward()
		else:
				accelerator.backward(UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple       , UpperCamelCase__:				List[Any]=False     ):
		set_seed(42     )
		SCREAMING_SNAKE_CASE__								=   RegressionModel()
		SCREAMING_SNAKE_CASE__								=   deepcopy(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   RegressionDataset(length=80     )
		SCREAMING_SNAKE_CASE__								=   DataLoader(UpperCamelCase__       , batch_size=16     )
		model.to(accelerator.device     )
		if sched:
				SCREAMING_SNAKE_CASE__								=   AdamW(params=model.parameters()       , lr=1e-3     )
				SCREAMING_SNAKE_CASE__								=   AdamW(params=ddp_model.parameters()       , lr=1e-3     )
				SCREAMING_SNAKE_CASE__								=   LambdaLR(UpperCamelCase__       , lr_lambda=lambda UpperCamelCase__     : epoch**0.6_5     )
				SCREAMING_SNAKE_CASE__								=   LambdaLR(UpperCamelCase__       , lr_lambda=lambda UpperCamelCase__     : epoch**0.6_5     )
		# Make a copy of `model`
		if sched:
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.prepare(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
		else:
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.prepare(UpperCamelCase__       , UpperCamelCase__     )
		if sched:
				return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
		return model, ddp_model, dataloader






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple     ):
		# Test when on a single CPU or GPU that the context manager does nothing
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__     )
		# Use a single batch
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   next(iter(UpperCamelCase__     )     ).values()
		for iteration in range(3     ):
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   input.to(accelerator.device     ), target.to(accelerator.device     )
				# Perform our initial ground truth step in non "DDP"
				step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				# Do "gradient accumulation" (noop)
				if iteration % 2 == 0:
						# Accumulate grads locally
						with accelerator.no_sync(UpperCamelCase__     ):
								step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				else:
						# Sync grads
						step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )

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






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]     ):
		# Test on distributed setup that context manager behaves properly
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__     )
		# Use a single batch
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   next(iter(UpperCamelCase__     )     ).values()
		for iteration in range(3     ):
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   input.to(accelerator.device     ), target.to(accelerator.device     )
				# Perform our initial ground truth step in non "DDP"
				step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				# Do "gradient accumulation" (noop)
				if iteration % 2 == 0:
						# Accumulate grads locally
						with accelerator.no_sync(UpperCamelCase__     ):
								step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				else:
						# Sync grads
						step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )

				# 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     )
				SCREAMING_SNAKE_CASE__								=   ddp_input[torch.randperm(len(UpperCamelCase__     )     )]






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int=False       , UpperCamelCase__:				Union[str, Any]=False     ):
		SCREAMING_SNAKE_CASE__								=   Accelerator(
		    split_batches=UpperCamelCase__       , dispatch_batches=UpperCamelCase__       , gradient_accumulation_steps=2     )
		# Test that context manager behaves properly
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__     )
		for iteration, batch in enumerate(UpperCamelCase__     ):
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   batch.values()
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   input.to(accelerator.device     ), target.to(accelerator.device     )
				# Perform our initial ground truth step in non "DDP"
				step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				# Do "gradient accumulation" (noop)
				with accelerator.accumulate(UpperCamelCase__     ):
						step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )

				# 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(UpperCamelCase__     ) - 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     )
				SCREAMING_SNAKE_CASE__								=   ddp_input[torch.randperm(len(UpperCamelCase__     )     )]
		GradientState._reset_state()






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple=False       , UpperCamelCase__:				List[str]=False     ):
		SCREAMING_SNAKE_CASE__								=   Accelerator(
		    split_batches=UpperCamelCase__       , dispatch_batches=UpperCamelCase__       , gradient_accumulation_steps=2     )
		# Test that context manager behaves properly
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__       , UpperCamelCase__     )
		for iteration, batch in enumerate(UpperCamelCase__     ):
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   batch.values()
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_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(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				opt.step()

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






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   Accelerator()

		SCREAMING_SNAKE_CASE__								=   RegressionDataset(length=80     )
		SCREAMING_SNAKE_CASE__								=   DataLoader(UpperCamelCase__       , batch_size=16     )
		SCREAMING_SNAKE_CASE__								=   RegressionDataset(length=96     )
		SCREAMING_SNAKE_CASE__								=   DataLoader(UpperCamelCase__       , batch_size=16     )
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.prepare(UpperCamelCase__       , UpperCamelCase__     )
		assert accelerator.gradient_state.active_dataloader is None
		for iteration, _ in enumerate(UpperCamelCase__     ):
				assert id(accelerator.gradient_state.active_dataloader     ) == id(UpperCamelCase__     )
				if iteration < len(UpperCamelCase__     ) - 1:
						assert not accelerator.gradient_state.end_of_dataloader
						if iteration == 1:
								for batch_num, _ in enumerate(UpperCamelCase__     ):
										assert id(accelerator.gradient_state.active_dataloader     ) == id(UpperCamelCase__     )
										if batch_num < len(UpperCamelCase__     ) - 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 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   Accelerator()
		SCREAMING_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(UpperCamelCase__     )
		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(UpperCamelCase__     )
		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(UpperCamelCase__       , UpperCamelCase__     )

    # 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(UpperCamelCase__       , UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]     ):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
	main()