





'''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 				_lowerCAmelCase  (			lowerCamelCase_     :					int						,    lowerCamelCase_     :					Optional[Any]						,    lowerCamelCase_     :					str						,    lowerCamelCase_     :					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 				_lowerCAmelCase  (			lowerCamelCase_     :					Optional[Any]						,    lowerCamelCase_     :					Union[str, Any]						,    lowerCamelCase_     :					Union[str, Any]						,    lowerCamelCase_     :					Dict						,    lowerCamelCase_     :					Optional[Any]=True			):
			model.train()
			__lowercase			  =							model(lowerCamelCase_			)
			__lowercase			  =							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_     :					List[str]						,    lowerCamelCase_     :					Dict=False			):
			set_seed(4_2			)
			__lowercase			  =							RegressionModel()
			__lowercase			  =							deepcopy(lowerCamelCase_			)
			__lowercase			  =							RegressionDataset(length=8_0			)
			__lowercase			  =							DataLoader(lowerCamelCase_						,    batch_size=1_6			)
			model.to(accelerator.device			)
			if sched:
						__lowercase			  =							AdamW(params=model.parameters()						,    lr=1E-3			)
						__lowercase			  =							AdamW(params=ddp_model.parameters()						,    lr=1E-3			)
						__lowercase			  =							LambdaLR(lowerCamelCase_						,    lr_lambda=lambda lowerCamelCase_			: epoch**0.65			)
						__lowercase			  =							LambdaLR(lowerCamelCase_						,    lr_lambda=lambda lowerCamelCase_			: epoch**0.65			)
			# Make a copy of `model`
			if sched:
						__lowercase  ,						__lowercase  ,						__lowercase  ,						__lowercase			  =							accelerator.prepare(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)
			else:
						__lowercase  ,						__lowercase			  =							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_     :					str			):
			# Test when on a single CPU or GPU that the context manager does nothing
			__lowercase  ,						__lowercase  ,						__lowercase			  =							get_training_setup(lowerCamelCase_			)
			# Use a single batch
			__lowercase  ,						__lowercase			  =							next(iter(lowerCamelCase_			)			).values()
			for iteration in range(3			):
						# Gather the distributed inputs and targs for the base model
						__lowercase  ,						__lowercase			  =							accelerator.gather((ddp_input, ddp_target)			)
						__lowercase  ,						__lowercase			  =							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_3_3_7 + iteration			)
						__lowercase			  =							ddp_input[torch.randperm(len(lowerCamelCase_			)			)]




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Optional[int]			):
			# Test on distributed setup that context manager behaves properly
			__lowercase  ,						__lowercase  ,						__lowercase			  =							get_training_setup(lowerCamelCase_			)
			# Use a single batch
			__lowercase  ,						__lowercase			  =							next(iter(lowerCamelCase_			)			).values()
			for iteration in range(3			):
						# Gather the distributed inputs and targs for the base model
						__lowercase  ,						__lowercase			  =							accelerator.gather((ddp_input, ddp_target)			)
						__lowercase  ,						__lowercase			  =							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_3_3_7 + iteration			)
						__lowercase			  =							ddp_input[torch.randperm(len(lowerCamelCase_			)			)]




def 				_lowerCAmelCase  (			lowerCamelCase_     :					int=False						,    lowerCamelCase_     :					List[str]=False			):
			__lowercase			  =							Accelerator(
			    split_batches=lowerCamelCase_						,    dispatch_batches=lowerCamelCase_						,    gradient_accumulation_steps=2			)
			# Test that context manager behaves properly
			__lowercase  ,						__lowercase  ,						__lowercase			  =							get_training_setup(lowerCamelCase_			)
			for iteration, batch in enumerate(lowerCamelCase_			):
						__lowercase  ,						__lowercase			  =							batch.values()
						# Gather the distributed inputs and targs for the base model
						__lowercase  ,						__lowercase			  =							accelerator.gather((ddp_input, ddp_target)			)
						__lowercase  ,						__lowercase			  =							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_3_3_7 + iteration			)
						__lowercase			  =							ddp_input[torch.randperm(len(lowerCamelCase_			)			)]
			GradientState._reset_state()




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Optional[int]=False						,    lowerCamelCase_     :					Union[str, Any]=False			):
			__lowercase			  =							Accelerator(
			    split_batches=lowerCamelCase_						,    dispatch_batches=lowerCamelCase_						,    gradient_accumulation_steps=2			)
			# Test that context manager behaves properly
			__lowercase  ,						__lowercase  ,						__lowercase  ,						__lowercase  ,						__lowercase  ,						__lowercase  ,						__lowercase			  =							get_training_setup(lowerCamelCase_						,    lowerCamelCase_			)
			for iteration, batch in enumerate(lowerCamelCase_			):
						__lowercase  ,						__lowercase			  =							batch.values()
						# Gather the distributed inputs and targs for the base model
						__lowercase  ,						__lowercase			  =							accelerator.gather((ddp_input, ddp_target)			)
						__lowercase  ,						__lowercase			  =							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			  =							(((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_3_3_7 + iteration			)
			GradientState._reset_state()




def 				_lowerCAmelCase  (			):
			__lowercase			  =							Accelerator()

			__lowercase			  =							RegressionDataset(length=8_0			)
			__lowercase			  =							DataLoader(lowerCamelCase_						,    batch_size=1_6			)
			__lowercase			  =							RegressionDataset(length=9_6			)
			__lowercase			  =							DataLoader(lowerCamelCase_						,    batch_size=1_6			)
			__lowercase  ,						__lowercase			  =							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  (			):
			__lowercase			  =							Accelerator()
			__lowercase			  =							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_     :					Any			):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
	main()












'''simple docstring'''



_SCREAMING_SNAKE_CASE   =      {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}




def 				_lowerCAmelCase  (			lowerCamelCase_     :					dict						,    lowerCamelCase_     :					Optional[Any]						,    lowerCamelCase_     :					List[str]			):
			__lowercase			  =							set()
			# keep track of all the paths to be checked
			__lowercase			  =							[[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						__lowercase			  =							queue.pop(0			)
						# get the last node from the path
						__lowercase			  =							path[-1]
						if node not in explored:
									__lowercase			  =							graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												__lowercase			  =							list(lowerCamelCase_			)
												new_path.append(lowerCamelCase_			)
												queue.append(lowerCamelCase_			)
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(lowerCamelCase_			)

    # in case there's no path between the 2 nodes
			return []




def 				_lowerCAmelCase  (			lowerCamelCase_     :					dict						,    lowerCamelCase_     :					str						,    lowerCamelCase_     :					str			):
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			__lowercase			  =							[start]
			__lowercase			  =							set(lowerCamelCase_			)
			# Keep tab on distances from `start` node.
			__lowercase			  =							{start: 0, target: -1}
			while queue:
						__lowercase			  =							queue.pop(0			)
						if node == target:
									__lowercase			  =							(
									    dist[node] if dist[target] == -1 else min(dist[target]						,    dist[node]			)
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(lowerCamelCase_			)
												queue.append(lowerCamelCase_			)
												__lowercase			  =							dist[node] + 1
			return dist[target]


if __name__ == "__main__":
	print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
	print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4





