
'''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_(     A						:		str		,     A						:		Dict		,     A						:		Optional[Any]		,     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						:		Union[str, Any]		,     A						:		List[Any]		,     A						:		Optional[Any]		,     A						:		Optional[Any]		,     A						:		Tuple=True):
							model.train()
							UpperCamelCase   			=     model(A)
							UpperCamelCase   			=     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						:		str		,     A						:		List[str]=False):
							set_seed(42)
							UpperCamelCase   			=     RegressionModel()
							UpperCamelCase   			=     deepcopy(A)
							UpperCamelCase   			=     RegressionDataset(length=80)
							UpperCamelCase   			=     DataLoader(A		,     batch_size=16)
							model.to(accelerator.device)
							if sched:
														UpperCamelCase   			=     AdamW(params=model.parameters()		,     lr=1E-3)
														UpperCamelCase   			=     AdamW(params=ddp_model.parameters()		,     lr=1E-3)
														UpperCamelCase   			=     LambdaLR(A		,     lr_lambda=lambda A: epoch**0.65)
														UpperCamelCase   			=     LambdaLR(A		,     lr_lambda=lambda A: epoch**0.65)
							# Make a copy of `model`
							if sched:
														UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase   			=     accelerator.prepare(A		,     A		,     A		,     A)
							else:
														UpperCamelCase			,	UpperCamelCase   			=     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   			=     get_training_setup(A)
							# Use a single batch
							UpperCamelCase			,	UpperCamelCase   			=     next(iter(A)).values()
							for iteration in range(3):
														# Gather the distributed inputs and targs for the base model
														UpperCamelCase			,	UpperCamelCase   			=     accelerator.gather((ddp_input, ddp_target))
														UpperCamelCase			,	UpperCamelCase   			=     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(1337 + iteration)
														UpperCamelCase   			=     ddp_input[torch.randperm(len(A))]







def 		A_(     A						:		Optional[int]):
							# Test on distributed setup that context manager behaves properly
							UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase   			=     get_training_setup(A)
							# Use a single batch
							UpperCamelCase			,	UpperCamelCase   			=     next(iter(A)).values()
							for iteration in range(3):
														# Gather the distributed inputs and targs for the base model
														UpperCamelCase			,	UpperCamelCase   			=     accelerator.gather((ddp_input, ddp_target))
														UpperCamelCase			,	UpperCamelCase   			=     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(1337 + iteration)
														UpperCamelCase   			=     ddp_input[torch.randperm(len(A))]







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







def 		A_(     A						:		Any=False		,     A						:		Optional[int]=False):
							UpperCamelCase   			=     Accelerator(
							    split_batches=A		,     dispatch_batches=A		,     gradient_accumulation_steps=2)
							# Test that context manager behaves properly
							UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase   			=     get_training_setup(A		,     A)
							for iteration, batch in enumerate(A):
														UpperCamelCase			,	UpperCamelCase   			=     batch.values()
														# Gather the distributed inputs and targs for the base model
														UpperCamelCase			,	UpperCamelCase   			=     accelerator.gather((ddp_input, ddp_target))
														UpperCamelCase			,	UpperCamelCase   			=     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   			=     (((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(1337 + iteration)
							GradientState._reset_state()







def 		A_(     ):
							UpperCamelCase   			=     Accelerator()

							UpperCamelCase   			=     RegressionDataset(length=80)
							UpperCamelCase   			=     DataLoader(A		,     batch_size=16)
							UpperCamelCase   			=     RegressionDataset(length=96)
							UpperCamelCase   			=     DataLoader(A		,     batch_size=16)
							UpperCamelCase			,	UpperCamelCase   			=     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_(     ):
							UpperCamelCase   			=     Accelerator()
							UpperCamelCase   			=     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						:		List[str]):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
						main()






'''simple docstring'''
lowerCAmelCase					:    Optional[Any]           = {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}







def 		A_(     A						:		dict		,     A						:		str		,     A						:		Optional[Any]):
							UpperCamelCase   			=     set()
							# keep track of all the paths to be checked
							UpperCamelCase   			=     [[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
														UpperCamelCase   			=     queue.pop(0)
														# get the last node from the path
														UpperCamelCase   			=     path[-1]
														if node not in explored:
																					UpperCamelCase   			=     graph[node]
																					# go through all neighbour nodes, construct a new path and
																					# push it into the queue
																					for neighbour in neighbours:
																												UpperCamelCase   			=     list(A)
																												new_path.append(A)
																												queue.append(A)
																												# return path if neighbour is goal
																												if neighbour == goal:
																																			return new_path

            # mark node as explored
																					explored.add(A)

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







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




