

from __future__ import annotations

a_    :						Optional[Any]    		=      list[list[int]]

# assigning initial values to the grid
a_    :						Matrix    		=      [
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
a_    :						Matrix    		=      [
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]


def        __lowerCAmelCase    (       _UpperCamelCase		:  Matrix		,							_UpperCamelCase		:  int		,							_UpperCamelCase		:  int		,							_UpperCamelCase		:  int	)		->							bool:
 '''simple docstring'''


 for i in range(9	):
  if grid[row][i] == n or grid[i][column] == n:
   return False

 for i in range(3	):
  for j in range(3	):
   if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
    return False

 return True


def        __lowerCAmelCase    (       _UpperCamelCase		:  Matrix	)		->							tuple[int, int] | None:
 '''simple docstring'''


 for i in range(9	):
  for j in range(9	):
   if grid[i][j] == 0:
    return i, j
 return None


def        __lowerCAmelCase    (       _UpperCamelCase		:  Matrix	)		->							Matrix | None:
 '''simple docstring'''


 if location := find_empty_location(_UpperCamelCase	):
  SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			location
 else:
  # If the location is ``None``, then the grid is solved.
  return grid

 for digit in range(1		,							10	):
  if is_safe(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	):
   SCREAMING_SNAKE_CASE								=			digit

   if sudoku(_UpperCamelCase	) is not None:
    return grid

   SCREAMING_SNAKE_CASE								=			0

 return None


def        __lowerCAmelCase    (       _UpperCamelCase		:  Matrix	)		->							None:
 '''simple docstring'''


 for row in grid:
  for cell in row:
   print(_UpperCamelCase		,							end=' '	)
  print()


if __name__ == "__main__":
      # make a copy of grid so that you can compare with the unmodified grid
      for example_grid in (initial_grid, no_solution):
            print("\nExample grid:\n" + "=" * 20)
            print_solution(example_grid)
            print("\nExample grid solution:")
            a_    :						Union[str, Any]    		=      sudoku(example_grid)
            if solution is not None:
                  print_solution(solution)
            else:
                  print("Cannot find a solution.")




#
# 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        __lowerCAmelCase    (       *_UpperCamelCase		:  Optional[Any]	)		->							Optional[int]:
 '''simple docstring'''


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


a_    :						int    		=      int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
a_    :						str    		=      torch.device("cuda", local_rank)
a_    :						Optional[int]    		=      socket.gethostname()

a_    :						Union[str, Any]    		=      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
      a_    :						Dict    		=      dist.get_rank()
      a_    :						Any    		=      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

