import os

UpperCAmelCase_								=							{"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						Union[str, Any]:




       '''simple docstring'''






       _A=	0

       _A=	0
       while index < len(lowerCAmelCase_   ) - 1:
              _A=	SYMBOLS[numerals[index]]
              _A=	SYMBOLS[numerals[index + 1]]
              if current_value < next_value:
                     total_value -= current_value
              else:
                     total_value += current_value
              index += 1
       total_value += SYMBOLS[numerals[index]]

       return total_value

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						str:




       '''simple docstring'''






       _A=	''

       _A=	num // 10_00
       numerals += m_count * "M"
       num %= 10_00

       _A=	num // 1_00
       if c_count == 9:
              numerals += "CM"
              c_count -= 9
       elif c_count == 4:
              numerals += "CD"
              c_count -= 4
       if c_count >= 5:
              numerals += "D"
              c_count -= 5
       numerals += c_count * "C"
       num %= 1_00

       _A=	num // 10
       if x_count == 9:
              numerals += "XC"
              x_count -= 9
       elif x_count == 4:
              numerals += "XL"
              x_count -= 4
       if x_count >= 5:
              numerals += "L"
              x_count -= 5
       numerals += x_count * "X"
       num %= 10

       if num == 9:
              numerals += "IX"
              num -= 9
       elif num == 4:
              numerals += "IV"
              num -= 4
       if num >= 5:
              numerals += "V"
              num -= 5
       numerals += num * "I"

       return numerals

def 							UpperCamelCase				(    lowerCAmelCase_ = "/p089_roman.txt"   )		->						Optional[int]:




       '''simple docstring'''






       _A=	0

       with open(os.path.dirname(lowerCAmelCase_   ) + roman_numerals_filename   ) as filea:
              _A=	filea.readlines()

       for line in lines:
              _A=	line.strip()
              _A=	parse_roman_numerals(lowerCAmelCase_   )
              _A=	generate_roman_numerals(lowerCAmelCase_   )
              savings += len(lowerCAmelCase_   ) - len(lowerCAmelCase_   )

       return savings


if __name__ == "__main__":
      print(F"{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 							UpperCamelCase				(    *lowerCAmelCase_   )		->						Dict:




       '''simple docstring'''






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


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

UpperCAmelCase_								=							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
      UpperCAmelCase_								=							dist.get_rank()
      UpperCAmelCase_								=							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