

#
# 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_  (							*a  ):

      """simple docstring"""



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


lowerCAmelCase     :   Tuple						     =  int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
lowerCAmelCase     :   Tuple						     =  torch.device('cuda', local_rank)
lowerCAmelCase     :   List[Any]						     =  socket.gethostname()

lowerCAmelCase     :   Dict						     =  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
    lowerCAmelCase     :   Tuple						     =  dist.get_rank()
    lowerCAmelCase     :   int						     =  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 math

lowerCAmelCase     :   List[str]						     =  '2020.9.26'
lowerCAmelCase     :   List[Any]						     =  'xcodz-dot, cclaus, dhruvmanila'

def    A_  (							a					,   a					,   a					,   a					,   a  ):

      """simple docstring"""



      if not all(isinstance(a					,   (float, int)  ) for val in locals().values()  ):
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     f"Input values must either be float or int: {list(locals().values()  )}"
            raise TypeError(a  )
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     ((x * distance) / (z + distance)) * scale
      SCREAMING_SNAKE_CASE_				:  int           =     ((y * distance) / (z + distance)) * scale
      return projected_x, projected_y

def    A_  (							a					,   a					,   a					,   a					,   a  ):

      """simple docstring"""



      if not isinstance(a					,   a  ):
            raise TypeError('Axis must be a str'  )
      SCREAMING_SNAKE_CASE_				:  Any           =     locals()
      del input_variables["axis"]
      if not all(isinstance(a					,   (float, int)  ) for val in input_variables.values()  ):
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     (
                'Input values except axis must either be float or int: '
                f"{list(input_variables.values()  )}"
            )
            raise TypeError(a  )
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     (angle % 3_6_0) / 4_5_0 * 1_8_0 / math.pi
      if axis == "z":
            SCREAMING_SNAKE_CASE_				:  List[str]           =     x * math.cos(a  ) - y * math.sin(a  )
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     y * math.cos(a  ) + x * math.sin(a  )
            SCREAMING_SNAKE_CASE_				:  Dict           =     z
      elif axis == "x":
            SCREAMING_SNAKE_CASE_				:  Any           =     y * math.cos(a  ) - z * math.sin(a  )
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     z * math.cos(a  ) + y * math.sin(a  )
            SCREAMING_SNAKE_CASE_				:  str           =     x
      elif axis == "y":
            SCREAMING_SNAKE_CASE_				:  str           =     x * math.cos(a  ) - z * math.sin(a  )
            SCREAMING_SNAKE_CASE_				:  List[str]           =     z * math.cos(a  ) + x * math.sin(a  )
            SCREAMING_SNAKE_CASE_				:  Any           =     y
      else:
            raise ValueError('not a valid axis, choose one of \'x\', \'y\', \'z\''  )

      return new_x, new_y, new_z


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(F'{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }')
    print(F'{rotate(1.0, 2.0, 3.0, "y", 90.0) = }')






