





import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info







def  A					(				UpperCAmelCase					):






					class 							_a:







							def __init__(     self			,    __snake_case					)   ->   None:


												'''simple docstring'''

												assert isinstance(__snake_case			,    (bytes, str)					)
												_snake_case							:       List[str]												= content







							def 							lowercase							(     self					)   ->   int:


												'''simple docstring'''

												return json.loads(self.content					)

					def mock_response(*UpperCAmelCase	,	**UpperCAmelCase					):
										assert args[0] == AUTHENTICATED_USER_ENDPOINT
										assert "Authorization" in kwargs["headers"]
										assert kwargs["headers"]["Authorization"].startswith("token "					)
										assert "Accept" in kwargs["headers"]
										return FakeResponse(B"{\"login\":\"test\",\"id\":1}"					)

					monkeypatch.setattr(UpperCAmelCase	,	"get"	,	UpperCAmelCase					)
					_snake_case							:       Optional[int]												= fetch_github_info("token"					)
					assert result["login"] == "test"
					assert result["id"] == 1






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

import torch_xla.distributed.xla_multiprocessing as xmp







def  A					(				):
					_snake_case							:       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=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  A					(				):
					_snake_case							:       Any												= parse_args()

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

					# Patch sys.argv
					_snake_case							:       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()