



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

import torch_xla.distributed.xla_multiprocessing as xmp






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[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=_lowerCamelCase				, default=1				, help="Number of TPU cores to use (1 or 8)."  )

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

							return parser.parse_args()






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							List[str]    =    parse_args()

							# Import training_script as a module.
							_lowerCAmelCase     :							str    =    Path(args.training_script  )
							sys.path.append(str(script_fpath.parent.resolve()  )  )
							_lowerCAmelCase     :							List[Any]    =    script_fpath.stem
							_lowerCAmelCase     :							List[str]    =    importlib.import_module(_lowerCamelCase  )

							# Patch sys.argv
							_lowerCAmelCase     :							List[str]    =    [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()



"""simple docstring"""





# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def   __SCREAMING_SNAKE_CASE  (      A_=None ):
	if subparsers is not None:
		lowerCAmelCase__	:						Optional[Any]  				=	subparsers.add_parser('''test''' )
	else:
		lowerCAmelCase__	:						List[str]  				=	argparse.ArgumentParser('''Accelerate test command''' )

	parser.add_argument(
	    '''--config_file'''						,     default=A_						,     help=(
	        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
	        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
	        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
	        '''with \'huggingface\'.'''
	    )						,     )

	if subparsers is not None:
		parser.set_defaults(func=A_ )
	return parser

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						Optional[int]  				=	os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py'''] )

	if args.config_file is None:
		lowerCAmelCase__	:						Optional[Any]  				=	script_name
	else:
		lowerCAmelCase__	:						Any  				=	f'--config_file={args.config_file} {script_name}'

	lowerCAmelCase__	:						List[Any]  				=	['''accelerate-launch'''] + test_args.split()
	lowerCAmelCase__	:						int  				=	execute_subprocess_async(A_						,     env=os.environ.copy() )
	if result.returncode == 0:
		print('''Test is a success! You are ready for your distributed training!''' )

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						Any  				=	test_command_parser()
	lowerCAmelCase__	:						List[Any]  				=	parser.parse_args()
	test_command(A_ )


if __name__ == "__main__":
							main()




