


'''simple docstring'''



from __future__ import annotations

import time

import numpy as np

__lowercase	:						Tuple       =	[8, 5, 9, 7]
__lowercase	:						str       =	[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__lowercase	:						List[Any]       =	[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]





class 							__UpperCamelCase				:




	def __init__(   self						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       Optional[int]					    =  claim_vector
						__a			:       int					    =  allocated_resources_table
						__a			:       Optional[Any]					    =  maximum_claim_table




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return [
						    sum(p_item[i] for p_item in self.__allocated_resources_table							)
						    for i in range(len(self.__allocated_resources_table[0]							)							)
						]




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return np.array(self.__claim_vector							) - np.array(
						    self.__processes_resource_summation()							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return [
						    list(np.array(self.__maximum_claim_table[i]							) - np.array(__a							)							)
						    for i, allocated_resource in enumerate(self.__allocated_resources_table							)
						]




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return {self.__need().index(__a							): i for i in self.__need()}




	def       __UpperCAmelCase							(   self						,				**__a							):

						'''simple docstring'''





						__a			:       List[str]					    =  self.__need()
						__a			:       Any					    =  self.__allocated_resources_table
						__a			:       int					    =  self.__available_resources()
						__a			:       Optional[Any]					    =  self.__need_index_manager()
						for kw, val in kwargs.items():
											if kw and val is True:
																self.__pretty_data()
						print('_' * 50 + '\n'							)
						while need_list:
											__a			:       List[Any]					    =  False
											for each_need in need_list:
																__a			:       Dict					    =  True
																for index, need in enumerate(__a							):
																					if need > available_resources[index]:
																										__a			:       int					    =  False
																										break
																if execution:
																					__a			:       Optional[Any]					    =  True
																					# get the original index of the process from ind_ctrl db
																					for original_need_index, need_clone in need_index_manager.items():
																										if each_need == need_clone:
																															__a			:       Union[str, Any]					    =  original_need_index
																					print(f"""Process {process_number + 1} is executing."""							)
																					# remove the process run from stack
																					need_list.remove(__a							)
																					# update available/freed resources stack
																					__a			:       Optional[int]					    =  np.array(__a							) + np.array(
																					    alloc_resources_table[process_number]							)
																					print(
																					    'Updated available resource stack for processes: '
																					    + ' '.join([str(__a							) for x in available_resources]							)							)
																					break
											if safe:
																print('The process is in a safe state.\n'							)
											else:
																print('System in unsafe state. Aborting...\n'							)
																break





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						print(' ' * 9 + 'Allocated Resource Table'							)
						for item in self.__allocated_resources_table:
											print(
											    f"""P{self.__allocated_resources_table.index(__a							) + 1}"""
											    + ' '.join(f"""{it:>8}""" for it in item							)
											    + '\n'							)
						print(' ' * 9 + 'System Resource Table'							)
						for item in self.__maximum_claim_table:
											print(
											    f"""P{self.__maximum_claim_table.index(__a							) + 1}"""
											    + ' '.join(f"""{it:>8}""" for it in item							)
											    + '\n'							)
						print(
						    'Current Usage by Active Processes: '
						    + ' '.join(str(__a							) for x in self.__claim_vector							)							)
						print(
						    'Initial Available Resources:       '
						    + ' '.join(str(__a							) for x in self.__available_resources()							)							)
						time.sleep(1							)


if __name__ == "__main__":
	import doctest

	doctest.testmod()







'''simple docstring'''



#
# 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							(*_SCREAMING_SNAKE_CASE      :   int   ):
					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   )


__lowercase	:						Dict       =	int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
__lowercase	:						Tuple       =	torch.device('cuda', local_rank)
__lowercase	:						Optional[int]       =	socket.gethostname()

__lowercase	:						List[str]       =	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
	__lowercase	:						str       =	dist.get_rank()
	__lowercase	:						Union[str, 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



