


#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist






def 		_A		(		*_lowercase )					-> Tuple:


     """simple docstring"""


     with open(_lowercase					,  'r' ) as fh:
          fcntl.flock(_lowercase					,  fcntl.LOCK_EX )
          try:
               print(*_lowercase )
          finally:
               fcntl.flock(_lowercase					,  fcntl.LOCK_UN )


__snake_case   							=						int(os.environ['''LOCAL_RANK'''])
torch.cuda.set_device(local_rank)
__snake_case   							=						torch.device('''cuda''', local_rank)
__snake_case   							=						socket.gethostname()

__snake_case   							=						f"""[{hostname}-{local_rank}]"""

try:
    # test distributed
    dist.init_process_group('''nccl''')
    dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
    dist.barrier()

    # test cuda is available and can allocate memory
    torch.cuda.is_available()
    torch.ones(1).cuda(local_rank)

    # global rank
    __snake_case   							=						dist.get_rank()
    __snake_case   							=						dist.get_world_size()

    printflock(f"""{gpu} is OK (global rank: {rank}/{world_size})""")

    dist.barrier()
    if rank == 0:
        printflock(f"""pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}""")

except Exception:
    printflock(f"""{gpu} is broken""")
    raise






from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
__snake_case   							=						0

__snake_case   							=						[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

__snake_case   							=						[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

__snake_case   							=						tuple[int, int]



class 	__lowerCamelCase			:







       def __init__(			self: str,A_: int,A_: int,A_: int,A_: int,A_: int,A_: Node | None,):






            '''simple docstring'''




            __UpperCamelCase =			pos_x
            __UpperCamelCase =			pos_y
            __UpperCamelCase =			(pos_y, pos_x)
            __UpperCamelCase =			goal_x
            __UpperCamelCase =			goal_y
            __UpperCamelCase =			g_cost
            __UpperCamelCase =			parent
            __UpperCamelCase =			self.calculate_heuristic()
            __UpperCamelCase =			self.g_cost + self.h_cost







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			self.pos_x - self.goal_x
            __UpperCamelCase =			self.pos_y - self.goal_y
            if HEURISTIC == 1:
                 return abs(A_					) + abs(A_					)
            else:
                 return sqrt(dy**2 + dx**2					)







       def __lt__(			self: int,A_: Node					):






            '''simple docstring'''




            return self.f_cost < other.f_cost



class 	__lowerCamelCase			:







       def __init__(			self: Any,A_: TPosition,A_: TPosition					):






            '''simple docstring'''




            __UpperCamelCase =			Node(start[1],start[0],goal[1],goal[0],0,A_					)
            __UpperCamelCase =			Node(goal[1],goal[0],goal[1],goal[0],9_9999,A_					)

            __UpperCamelCase =			[self.start]
            __UpperCamelCase =			[]

            __UpperCamelCase =			False







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            while self.open_nodes:
                 # Open Nodes are sorted using __lt__
                 self.open_nodes.sort()
                 __UpperCamelCase =			self.open_nodes.pop(0					)

                 if current_node.pos == self.target.pos:
                      return self.retrace_path(A_					)

                 self.closed_nodes.append(A_					)
                 __UpperCamelCase =			self.get_successors(A_					)

                 for child_node in successors:
                      if child_node in self.closed_nodes:
                           continue

                      if child_node not in self.open_nodes:
                           self.open_nodes.append(A_					)
                      else:
                           # retrieve the best current path
                           __UpperCamelCase =			self.open_nodes.pop(self.open_nodes.index(A_					)					)

                           if child_node.g_cost < better_node.g_cost:
                                self.open_nodes.append(A_					)
                           else:
                                self.open_nodes.append(A_					)

            return [self.start.pos]







       def 		snake_case_	(			self: int,A_: Node					):






            '''simple docstring'''




            __UpperCamelCase =			[]
            for action in delta:
                 __UpperCamelCase =			parent.pos_x + action[1]
                 __UpperCamelCase =			parent.pos_y + action[0]
                 if not (0 <= pos_x <= len(grid[0]					) - 1 and 0 <= pos_y <= len(A_					) - 1):
                      continue

                 if grid[pos_y][pos_x] != 0:
                      continue

                 successors.append(
                     Node(
                         A_,A_,self.target.pos_y,self.target.pos_x,parent.g_cost + 1,A_,)					)
            return successors







       def 		snake_case_	(			self: Any,A_: Node | None					):






            '''simple docstring'''




            __UpperCamelCase =			node
            __UpperCamelCase =			[]
            while current_node is not None:
                 path.append((current_node.pos_y, current_node.pos_x)					)
                 __UpperCamelCase =			current_node.parent
            path.reverse()
            return path



class 	__lowerCamelCase			:







       def __init__(			self: List[Any],A_: TPosition,A_: TPosition					):






            '''simple docstring'''




            __UpperCamelCase =			AStar(A_,A_					)
            __UpperCamelCase =			AStar(A_,A_					)
            __UpperCamelCase =			False







       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
                 self.fwd_astar.open_nodes.sort()
                 self.bwd_astar.open_nodes.sort()
                 __UpperCamelCase =			self.fwd_astar.open_nodes.pop(0					)
                 __UpperCamelCase =			self.bwd_astar.open_nodes.pop(0					)

                 if current_bwd_node.pos == current_fwd_node.pos:
                      return self.retrace_bidirectional_path(
                          A_,A_					)

                 self.fwd_astar.closed_nodes.append(A_					)
                 self.bwd_astar.closed_nodes.append(A_					)

                 __UpperCamelCase =			current_bwd_node
                 __UpperCamelCase =			current_fwd_node

                 __UpperCamelCase =			{
                     self.fwd_astar: self.fwd_astar.get_successors(A_					),
                     self.bwd_astar: self.bwd_astar.get_successors(A_					),
                 }

                 for astar in [self.fwd_astar, self.bwd_astar]:
                      for child_node in successors[astar]:
                           if child_node in astar.closed_nodes:
                                continue

                           if child_node not in astar.open_nodes:
                                astar.open_nodes.append(A_					)
                           else:
                                # retrieve the best current path
                                __UpperCamelCase =			astar.open_nodes.pop(
                                    astar.open_nodes.index(A_					)					)

                                if child_node.g_cost < better_node.g_cost:
                                     astar.open_nodes.append(A_					)
                                else:
                                     astar.open_nodes.append(A_					)

            return [self.fwd_astar.start.pos]







       def 		snake_case_	(			self: List[str],A_: Node,A_: Node					):






            '''simple docstring'''




            __UpperCamelCase =			self.fwd_astar.retrace_path(A_					)
            __UpperCamelCase =			self.bwd_astar.retrace_path(A_					)
            bwd_path.pop()
            bwd_path.reverse()
            __UpperCamelCase =			fwd_path + bwd_path
            return path


if __name__ == "__main__":
    # all coordinates are given in format [y,x]
    __snake_case   							=						(0, 0)
    __snake_case   							=						(len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    __snake_case   							=						time.time()
    __snake_case   							=						AStar(init, goal)
    __snake_case   							=						a_star.search()
    __snake_case   							=						time.time() - start_time
    print(f"""AStar execution time = {end_time:f} seconds""")

    __snake_case   							=						time.time()
    __snake_case   							=						BidirectionalAStar(init, goal)
    __snake_case   							=						time.time() - bd_start_time
    print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")


