
#
# 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 						_snake_case(  *SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:





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


lowercase  :					str						    =							int(os.environ["""LOCAL_RANK"""])
torch.cuda.set_device(local_rank)
lowercase  :					Union[str, Any]						    =							torch.device("""cuda""", local_rank)
lowercase  :					Tuple						    =							socket.gethostname()

lowercase  :					Union[str, 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  :					Optional[Any]						    =							dist.get_rank()
  lowercase  :					Optional[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





import glob
import os
import random
from string import ascii_lowercase, digits

import cva
__A   			=""
__A   			=""
__A   			=""
__A   			=1  # (0 is vertical, 1 is horizontal)

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	get_dataset(_UpperCAmelCase ,   _UpperCAmelCase						)
		print('''Processing...'''						)
		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     List[Any]													=	update_image_and_anno(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)

		for index, image in enumerate(_UpperCAmelCase						):
				# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
				__UpperCAmelCase    :     Any													=	random_chars(32						)
				__UpperCAmelCase    :     List[str]													=	paths[index].split(os.sep						)[-1].rsplit('''.''' ,   1						)[0]
				__UpperCAmelCase    :     Optional[Any]													=	f'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
				cva.imwrite(f'/{file_root}.jpg' ,   _UpperCAmelCase ,   [cva.IMWRITE_JPEG_QUALITY, 85]						)
				print(f'Success {index+1}/{len(_UpperCAmelCase						)} with {file_name}'						)
				__UpperCAmelCase    :     Optional[Any]													=	[]
				for anno in new_annos[index]:
						__UpperCAmelCase    :     Union[str, Any]													=	f'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
						annos_list.append(_UpperCAmelCase						)
				with open(f'/{file_root}.txt' ,   '''w'''						) as outfile:
						outfile.write('''\n'''.join(line for line in annos_list						)						)

def        a						(      _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str						):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	[]
		__UpperCAmelCase    :     Any													=	[]
		for label_file in glob.glob(os.path.join(_UpperCAmelCase ,   '''*.txt'''						)						):
				__UpperCAmelCase    :     Optional[Any]													=	label_file.split(os.sep						)[-1].rsplit('''.''' ,   1						)[0]
				with open(_UpperCAmelCase						) as in_file:
						__UpperCAmelCase    :     List[str]													=	in_file.readlines()
				__UpperCAmelCase    :     Optional[Any]													=	os.path.join(_UpperCAmelCase ,   f'{label_name}.jpg'						)

				__UpperCAmelCase    :     str													=	[]
				for obj_list in obj_lists:
						__UpperCAmelCase    :     str													=	obj_list.rstrip('''\n'''						).split(''' '''						)
						boxes.append(
						    [
						        int(obj[0]						),
						        float(obj[1]						),
						        float(obj[2]						),
						        float(obj[3]						),
						        float(obj[4]						),
						    ]						)
				if not boxes:
						continue
				img_paths.append(_UpperCAmelCase						)
				labels.append(_UpperCAmelCase						)
		return img_paths, labels

def        a						(      _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					list ,   _UpperCAmelCase							:					int = 1						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	[]
		__UpperCAmelCase    :     Optional[Any]													=	[]
		__UpperCAmelCase    :     Any													=	[]
		for idx in range(len(_UpperCAmelCase						)						):
				__UpperCAmelCase    :     Tuple													=	[]
				__UpperCAmelCase    :     List[Any]													=	img_list[idx]
				path_list.append(_UpperCAmelCase						)
				__UpperCAmelCase    :     str													=	anno_list[idx]
				__UpperCAmelCase    :     str													=	cva.imread(_UpperCAmelCase						)
				if flip_type == 1:
						__UpperCAmelCase    :     Any													=	cva.flip(_UpperCAmelCase ,   _UpperCAmelCase						)
						for bbox in img_annos:
								__UpperCAmelCase    :     List[str]													=	1 - bbox[1]
								new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]						)
				elif flip_type == 0:
						__UpperCAmelCase    :     Any													=	cva.flip(_UpperCAmelCase ,   _UpperCAmelCase						)
						for bbox in img_annos:
								__UpperCAmelCase    :     Union[str, Any]													=	1 - bbox[2]
								new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]						)
				new_annos_lists.append(_UpperCAmelCase						)
				new_imgs_list.append(_UpperCAmelCase						)
		return new_imgs_list, new_annos_lists, path_list

def        a						(      _UpperCAmelCase							:					int = 32						):







		'''simple docstring'''





		assert number_char > 1, "The number of character should greater than 1"
		__UpperCAmelCase    :     Union[str, Any]													=	ascii_lowercase + digits
		return "".join(random.choice(_UpperCAmelCase						) for _ in range(_UpperCAmelCase						)						)


if __name__ == "__main__":
				main()
				print("DONE ✅")





