"""simple docstring"""







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

import torch_xla.distributed.xla_multiprocessing as xmp
def   __UpperCAmelCase    (   )							->      str:

	'''simple docstring'''





	__snake_case      :							Tuple						=			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=_snake_case      ,							default=1      ,							help='Number of TPU cores to use (1 or 8).'  )

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

	return parser.parse_args()
def   __UpperCAmelCase    (   )							->      Optional[int]:

	'''simple docstring'''





	__snake_case      :							Dict						=			parse_args()

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

	# Patch sys.argv
	__snake_case      :							int						=			[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 warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_a					:					Dict=			logging.get_logger(__name__)

class 		UpperCamelCase      (   lowercase						):



				def __init__(self    :				List[str]     ,  *_A    :				Dict     ,  **_A    :				Optional[Any])   ->     None:
					warnings.warn(
					    'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
					    ' use CLIPImageProcessor instead.'     ,  _A     ,  )
					super().__init__(*_A     ,  **_A)





