





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase			:Tuple	 =     logging.get_logger(__name__)

__lowerCAmelCase			:Dict	 =     {
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}






class 							_a(     __A     ):
		lowerCamelCase__  :List[Any] 					=		'regnet'
		lowerCamelCase__  :Optional[int] 					=		['x', 'y']







		def __init__(     self			,    __snake_case=3			,    __snake_case=3_2			,    __snake_case=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]			,    __snake_case=[2, 6, 1_2, 2]			,    __snake_case=6_4			,    __snake_case="y"			,    __snake_case="relu"			,    **__snake_case			,    )   ->   Any:


							'''simple docstring'''

							super().__init__(**__snake_case					)
							if layer_type not in self.layer_types:
												raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types					)}"""					)
							_snake_case							:       int												= num_channels
							_snake_case							:       Optional[Any]												= embedding_size
							_snake_case							:       Optional[Any]												= hidden_sizes
							_snake_case							:       List[Any]												= depths
							_snake_case							:       Dict												= groups_width
							_snake_case							:       List[str]												= layer_type
							_snake_case							:       Union[str, Any]												= hidden_act
							# always downsample in the first stage
							_snake_case							:       str												= True






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()