


'''simple docstring'''
import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
UpperCAmelCase_			:  Any										=   logging.getLogger(__name__)


def       _lowercase							(					UpperCamelCase__							:       str      ):
 __A	:		int	  =      git.Repo(search_parent_directories=UpperCamelCase__      )
 __A	:		Optional[Any]	  =      {
     'repo_id': str(UpperCamelCase__      ),
     'repo_sha': str(repo.head.object.hexsha      ),
     'repo_branch': str(repo.active_branch      ),
 }

 with open(os.path.join(UpperCamelCase__,					'git_log.json'      ),					'w'      ) as f:
  json.dump(UpperCamelCase__,					UpperCamelCase__,					indent=4      )


def       _lowercase							(					UpperCamelCase__							:       Optional[Any]      ):
 if params.n_gpu <= 0:
  __A	:		List[str]	  =      0
  __A	:		List[Any]	  =      -1
  __A	:		str	  =      True
  __A	:		List[Any]	  =      False
  return

 assert torch.cuda.is_available()

 logger.info('Initializing GPUs'      )
 if params.n_gpu > 1:
  assert params.local_rank != -1

  __A	:		int	  =      int(os.environ['WORLD_SIZE']      )
  __A	:		Optional[int]	  =      int(os.environ['N_GPU_NODE']      )
  __A	:		Dict	  =      int(os.environ['RANK']      )

  # number of nodes / node ID
  __A	:		List[Any]	  =      params.world_size // params.n_gpu_per_node
  __A	:		Union[str, Any]	  =      params.global_rank // params.n_gpu_per_node
  __A	:		str	  =      True

  assert params.n_nodes == int(os.environ['N_NODES']      )
  assert params.node_id == int(os.environ['NODE_RANK']      )

 # local job (single GPU)
 else:
  assert params.local_rank == -1

  __A	:		List[Any]	  =      1
  __A	:		List[Any]	  =      0
  __A	:		Optional[Any]	  =      0
  __A	:		Optional[int]	  =      0
  __A	:		Dict	  =      1
  __A	:		int	  =      1
  __A	:		int	  =      False

 # sanity checks
 assert params.n_nodes >= 1
 assert 0 <= params.node_id < params.n_nodes
 assert 0 <= params.local_rank <= params.global_rank < params.world_size
 assert params.world_size == params.n_nodes * params.n_gpu_per_node

 # define whether this is the master process / if we are in multi-node distributed mode
 __A	:		Any	  =      params.node_id == 0 and params.local_rank == 0
 __A	:		Any	  =      params.n_nodes > 1

 # summary
 __A	:		Union[str, Any]	  =      f"""--- Global rank: {params.global_rank} - """
 logger.info(PREFIX + 'Number of nodes: %i' % params.n_nodes      )
 logger.info(PREFIX + 'Node ID        : %i' % params.node_id      )
 logger.info(PREFIX + 'Local rank     : %i' % params.local_rank      )
 logger.info(PREFIX + 'World size     : %i' % params.world_size      )
 logger.info(PREFIX + 'GPUs per node  : %i' % params.n_gpu_per_node      )
 logger.info(PREFIX + 'Master         : %s' % str(params.is_master      )      )
 logger.info(PREFIX + 'Multi-node     : %s' % str(params.multi_node      )      )
 logger.info(PREFIX + 'Multi-GPU      : %s' % str(params.multi_gpu      )      )
 logger.info(PREFIX + 'Hostname       : %s' % socket.gethostname()      )

 # set GPU device
 torch.cuda.set_device(params.local_rank      )

 # initialize multi-GPU
 if params.multi_gpu:
  logger.info('Initializing PyTorch distributed'      )
  torch.distributed.init_process_group(
      init_method='env://',					backend='nccl',					)


def       _lowercase							(					UpperCamelCase__							:       Optional[int]      ):
 np.random.seed(args.seed      )
 torch.manual_seed(args.seed      )
 if args.n_gpu > 0:
  torch.cuda.manual_seed_all(args.seed      )










'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def       _lowercase							(					):
 __A	:		List[Any]	  =      ArgumentParser(
     description=(
         'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
     )      )

 # Optional arguments for the launch helper
 parser.add_argument('--num_cores',					type=UpperCamelCase__,					default=1,					help='Number of TPU cores to use (1 or 8).'      )

 # positional
 parser.add_argument(
     'training_script',					type=UpperCamelCase__,					help=(
         'The full path to the single TPU training '
         'program/script to be launched in parallel, '
         'followed by all the arguments for the '
         'training script'
     ),					)

 # rest from the training program
 parser.add_argument('training_script_args',					nargs=UpperCamelCase__      )

 return parser.parse_args()


def       _lowercase							(					):
 __A	:		str	  =      parse_args()

 # Import training_script as a module.
 __A	:		Tuple	  =      Path(args.training_script      )
 sys.path.append(str(script_fpath.parent.resolve()      )      )
 __A	:		str	  =      script_fpath.stem
 __A	:		Dict	  =      importlib.import_module(UpperCamelCase__      )

 # Patch sys.argv
 __A	:		Optional[Any]	  =      [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores      )]

 xmp.spawn(mod._mp_fn,					args=(),					nprocs=args.num_cores      )


if __name__ == "__main__":
 main()






