




from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


__UpperCamelCase					:       Optional[int]    					=      datasets.utils.logging.get_logger(__name__)

class 		__lowerCAmelCase	(	folder_based_builder.FolderBasedBuilderConfig ):
							UpperCamelCase__					     =  None
							UpperCamelCase__					     =  None







class 		__lowerCAmelCase	(	folder_based_builder.FolderBasedBuilder ):
							UpperCamelCase__					     =  datasets.Audio()
							UpperCamelCase__					     =  '''audio'''
							UpperCamelCase__					     =  AudioFolderConfig
							UpperCamelCase__					     =  42  # definition at the bottom of the script
							UpperCamelCase__					     =  AudioClassification(audio_column='''audio'''      ,       label_column='''label''' )







__UpperCamelCase					:       str    					=      [
    ".aiff",
    ".au",
    ".avr",
    ".caf",
    ".flac",
    ".htk",
    ".svx",
    ".mat4",
    ".mat5",
    ".mpc2k",
    ".ogg",
    ".paf",
    ".pvf",
    ".raw",
    ".rf64",
    ".sd2",
    ".sds",
    ".ircam",
    ".voc",
    ".w64",
    ".wav",
    ".nist",
    ".wavex",
    ".wve",
    ".xi",
    ".mp3",
    ".opus",
]
__UpperCamelCase					:       int    					=      AUDIO_EXTENSIONS







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

import torch_xla.distributed.xla_multiprocessing as xmp





def 		__A		(	)      ->       Any:
	a										=							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=__lowerCamelCase							,			default=1							,			help="""Number of TPU cores to use (1 or 8)."""    )

	# positional
	parser.add_argument(
	    """training_script"""							,			type=__lowerCamelCase							,			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=__lowerCamelCase    )

	return parser.parse_args()





def 		__A		(	)      ->       Union[str, Any]:
	a										=							parse_args()

	# Import training_script as a module.
	a										=							Path(args.training_script    )
	sys.path.append(str(script_fpath.parent.resolve()    )    )
	a										=							script_fpath.stem
	a										=							importlib.import_module(__lowerCamelCase    )

	# Patch sys.argv
	a										=							[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()

