
#
# 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__							(					*a      )      ->   Optional[Any]:
   with open(a		,		'''r'''      ) as fh:
      fcntl.flock(a		,		fcntl.LOCK_EX      )
      try:
         print(*a      )
      finally:
         fcntl.flock(a		,		fcntl.LOCK_UN      )


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

UpperCAmelCase__		:     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
   UpperCAmelCase__		:     Optional[int]     			=      dist.get_rank()
   UpperCAmelCase__		:     Dict     			=      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 sys
from collections import deque
from typing import Generic, TypeVar

UpperCAmelCase__		:     Union[str, Any]     			=      TypeVar('T')






class UpperCAmelCase   (						Generic[T]   ):





   '''simple docstring'''
   __UpperCamelCase		:    deque[T]  # Cache store of keys
   __UpperCamelCase		:    set[T]  # References of the keys in cache
   __UpperCamelCase		:    int 				=    10  # Maximum capacity of cache


   def __init__(					self     :							List[str]			,			lowerCAmelCase_     :							int     ):

      """simple docstring"""
      _A:  Tuple					=       deque()
      _A:  List[Any]					=       set()
      if not n:
         _A:  str					=       sys.maxsize
      elif n < 0:
         raise ValueError('''n should be an integer greater than 0.'''     )
      else:
         _A:  Dict					=       n


   def  __magic_name__				(					self     :							Optional[int]			,			lowerCAmelCase_     :							T     ):

      """simple docstring"""
      if x not in self.key_reference:
         if len(self.dq_store     ) == LRUCache._MAX_CAPACITY:
            _A:  Optional[Any]					=       self.dq_store.pop()
            self.key_reference.remove(lowerCAmelCase_     )
      else:
         self.dq_store.remove(lowerCAmelCase_     )

      self.dq_store.appendleft(lowerCAmelCase_     )
      self.key_reference.add(lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      for k in self.dq_store:
         print(lowerCAmelCase_     )


   def __repr__(					self     :							Dict     ):

      """simple docstring"""
      return F"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store     )}"""


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase__		:     LRUCache[str | int]     			=      LRUCache(4)
   lru_cache.refer('A')
   lru_cache.refer(2)
   lru_cache.refer(3)
   lru_cache.refer('A')
   lru_cache.refer(4)
   lru_cache.refer(5)
   lru_cache.display()

   print(lru_cache)
   assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"



