





"""simple docstring"""



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

import torch_xla.distributed.xla_multiprocessing as xmp






def 	__snake_case  (    ):






							"""simple docstring"""



							_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=SCREAMING_SNAKE_CASE     ,  default=1     ,  help='Number of TPU cores to use (1 or 8).'						)

							# positional
							parser.add_argument(
							    'training_script'     ,  type=SCREAMING_SNAKE_CASE     ,  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=SCREAMING_SNAKE_CASE						)

							return parser.parse_args()






def 	__snake_case  (    ):






							"""simple docstring"""



							_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(SCREAMING_SNAKE_CASE						)

							# 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()








"""simple docstring"""



import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase							)
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							str							=					field(default="image-classification"      ,				metadata={"include_in_asdict_even_if_is_default": True}							)
				SCREAMING_SNAKE_CASE_:							ClassVar[Features]							=					Features({"image": Image()}							)
				SCREAMING_SNAKE_CASE_:							ClassVar[Features]							=					Features({"labels": ClassLabel}							)
				SCREAMING_SNAKE_CASE_:							str							=					"image"
				SCREAMING_SNAKE_CASE_:							str							=					"labels"


				def 				__lowerCamelCase	(					self	:			str				,     UpperCAmelCase_	:			Optional[Any]  )		->  Dict:







											"""simple docstring"""




											if self.label_column not in features:
																		raise ValueError(F"""Column {self.label_column} is not present in features."""  )
											if not isinstance(features[self.label_column]				,     UpperCAmelCase_  ):
																		raise ValueError(F"""Column {self.label_column} is not a ClassLabel."""  )
											_lowerCAmelCase  	=			copy.deepcopy(self  )
											_lowerCAmelCase  	=			self.label_schema.copy()
											_lowerCAmelCase  	=			features[self.label_column]
											_lowerCAmelCase  	=			label_schema
											return task_template







				@property
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Dict[str, str]:







											"""simple docstring"""




											return {
											    self.image_column: "image",
											    self.label_column: "labels",
											}






