




#
# 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      (						*SCREAMING_SNAKE_CASE_							:				List[Any]     ):
					with open(SCREAMING_SNAKE_CASE_      ,      "r"     ) as fh:
										fcntl.flock(SCREAMING_SNAKE_CASE_      ,      fcntl.LOCK_EX     )
										try:
															print(*SCREAMING_SNAKE_CASE_     )
										finally:
															fcntl.flock(SCREAMING_SNAKE_CASE_      ,      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










from random import randint
from tempfile import TemporaryFile

import numpy as np







def 	_a      (						SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				List[str]     ):
					__lowerCAmelCase				   =       0
					if start < end:
										__lowerCAmelCase				   =       randint(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
										__lowerCAmelCase				   =       a[end]
										__lowerCAmelCase				   =       a[pivot]
										__lowerCAmelCase				   =       temp

										__lowerCAmelCase   ,							__lowerCAmelCase				   =       _in_place_partition(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      p - 1     )
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_      ,      p + 1      ,      SCREAMING_SNAKE_CASE_     )
					return count







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				List[Any]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					__lowerCAmelCase				   =       0
					__lowerCAmelCase				   =       randint(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       a[end]
					__lowerCAmelCase				   =       a[pivot]
					__lowerCAmelCase				   =       temp
					__lowerCAmelCase				   =       start - 1
					for index in range(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     ):
										count += 1
										if a[index] < a[end]:  # check if current val is less than pivot value
															__lowerCAmelCase				   =       new_pivot_index + 1
															__lowerCAmelCase				   =       a[new_pivot_index]
															__lowerCAmelCase				   =       a[index]
															__lowerCAmelCase				   =       temp

					__lowerCAmelCase				   =       a[new_pivot_index + 1]
					__lowerCAmelCase				   =       a[end]
					__lowerCAmelCase				   =       temp
					return new_pivot_index + 1, count


UpperCamelCase__    			=			TemporaryFile()
UpperCamelCase__    			=			100  # 1000 elements are to be sorted


UpperCamelCase__			,	UpperCamelCase__    			=			0, 1  # mean and standard deviation
UpperCamelCase__    			=			np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
UpperCamelCase__    			=			np.load(outfile)
UpperCamelCase__    			=			len(M) - 1
UpperCamelCase__    			=			_in_place_quick_sort(M, 0, r)

print(
    """No of Comparisons for 100 elements selected from a standard normal distribution"""
    """is :"""
)


print(z)




