





'''simple docstring'''







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment





class     _UpperCAmelCase							(						unittest.TestCase   ):
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				inspect.getfile(accelerate.test_utils			)
					__lowerCAmelCase											=				os.path.sep.join(mod_file.split(os.path.sep			)[:-1] + ["""scripts""", """test_sync.py"""]			)
		@require_cpu
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					debug_launcher(test_sync.main,num_processes=1			)
		@require_cpu
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					debug_launcher(test_sync.main			)
		@require_single_gpu
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					test_sync.main()







		@require_multi_gpu
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					print(f'Found {torch.cuda.device_count()} devices.'			)
					__lowerCAmelCase											=				["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
					with patch_environment(omp_num_threads=1			):
								execute_subprocess_async(__SCREAMING_SNAKE_CASE,env=os.environ.copy()			)








'''simple docstring'''







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp





def    _lowerCAmelCase						(						)					->					List[str]:
			__lowerCAmelCase											=				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=lowercase					,	default=1					,	help="""Number of TPU cores to use (1 or 8)."""	)

			# positional
			parser.add_argument(
			    """training_script"""					,	type=lowercase					,	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=lowercase	)

			return parser.parse_args()





def    _lowerCAmelCase						(						)					->					Any:
			__lowerCAmelCase											=				parse_args()

			# Import training_script as a module.
			__lowerCAmelCase											=				Path(args.training_script	)
			sys.path.append(str(script_fpath.parent.resolve()	)	)
			__lowerCAmelCase											=				script_fpath.stem
			__lowerCAmelCase											=				importlib.import_module(lowercase	)

			# Patch sys.argv
			__lowerCAmelCase											=				[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()

