


'''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__      (						*__lowerCamelCase	:							str							):







   '''simple docstring'''




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


lowercase							=int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
lowercase							=torch.device('cuda', local_rank)
lowercase							=socket.gethostname()

lowercase							=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							=dist.get_rank()
 lowercase							=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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase							=logging.get_logger(__name__)

lowercase							={
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="glpn"






       def __init__(      self  ,							snake_case=3  ,							snake_case=4  ,							snake_case=[2, 2, 2, 2]  ,							snake_case=[8, 4, 2, 1]  ,							snake_case=[3_2, 6_4, 1_6_0, 2_5_6]  ,							snake_case=[7, 3, 3, 3]  ,							snake_case=[4, 2, 2, 2]  ,							snake_case=[1, 2, 5, 8]  ,							snake_case=[4, 4, 4, 4]  ,							snake_case="gelu"  ,							snake_case=0.0  ,							snake_case=0.0  ,							snake_case=0.02  ,							snake_case=0.1  ,							snake_case=1E-6  ,							snake_case=6_4  ,							snake_case=1_0  ,							snake_case=-1  ,							**snake_case  ,							)						->  Tuple:




          '''simple docstring'''
          super().__init__(**snake_case)

          _UpperCAmelCase       :       Any							   =num_channels
          _UpperCAmelCase       :       List[str]							   =num_encoder_blocks
          _UpperCAmelCase       :       Optional[Any]							   =depths
          _UpperCAmelCase       :       str							   =sr_ratios
          _UpperCAmelCase       :       Dict							   =hidden_sizes
          _UpperCAmelCase       :       List[str]							   =patch_sizes
          _UpperCAmelCase       :       Any							   =strides
          _UpperCAmelCase       :       List[str]							   =mlp_ratios
          _UpperCAmelCase       :       Dict							   =num_attention_heads
          _UpperCAmelCase       :       List[str]							   =hidden_act
          _UpperCAmelCase       :       int							   =hidden_dropout_prob
          _UpperCAmelCase       :       List[Any]							   =attention_probs_dropout_prob
          _UpperCAmelCase       :       Union[str, Any]							   =initializer_range
          _UpperCAmelCase       :       Tuple							   =drop_path_rate
          _UpperCAmelCase       :       str							   =layer_norm_eps
          _UpperCAmelCase       :       Optional[int]							   =decoder_hidden_size
          _UpperCAmelCase       :       List[str]							   =max_depth
          _UpperCAmelCase       :       Dict							   =head_in_index

