





'''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      (  *__snake_case  :			List[Any]      )					->					List[str]:
     with open(__snake_case			,							'r'      ) as fh:
          fcntl.flock(__snake_case			,							fcntl.LOCK_EX      )
          try:
               print(*__snake_case      )
          finally:
               fcntl.flock(__snake_case			,							fcntl.LOCK_UN      )


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

lowercase__   :				List[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
  lowercase__   :				List[Any]      =   dist.get_rank()
  lowercase__   :				List[str]      =   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






'''simple docstring'''




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
  from .camera import create_pan_cameras
  from .pipeline_shap_e import ShapEPipeline
  from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
  from .renderer import (
      BoundingBoxVolume,
      ImportanceRaySampler,
      MLPNeRFModelOutput,
      MLPNeRSTFModel,
      ShapEParamsProjModel,
      ShapERenderer,
      StratifiedRaySampler,
      VoidNeRFModel,
  )