





import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


__lowerCAmelCase			:Any	 =     TypeVar('T', bound='TaskTemplate')






@dataclass(frozen=__A     )
class 							_a(     abc.ABC     ):
		# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
		lowerCamelCase__  :str
		lowerCamelCase__  :ClassVar[Features]
		lowerCamelCase__  :ClassVar[Features]







		def 							lowercase							(     self			,    __snake_case					)   ->   T:


							'''simple docstring'''

							return copy.deepcopy(self					)







		@property
		def 							lowercase							(     self					)   ->   Features:


							'''simple docstring'''

							return Features(**self.input_schema			,    **self.label_schema					)







		@property
		@abc.abstractmethod
		def 							lowercase							(     self					)   ->   Dict[str, str]:


							'''simple docstring'''

							raise NotImplementedError







		@classmethod
		def 							lowercase							(     cls			,    __snake_case					)   ->   T:


							'''simple docstring'''

							_snake_case							:       str												= {f.name for f in dataclasses.fields(cls					)}
							return cls(**{k: v for k, v in template_dict.items() if k in field_names}					)






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()