





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

import torch_xla.distributed.xla_multiprocessing as xmp


def 							__snake_case							(    )				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			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 							__snake_case							(    )				->						int:
    SCREAMING_SNAKE_CASE__											=			parse_args()

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

    # Patch sys.argv
    SCREAMING_SNAKE_CASE__											=			[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()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A					:				List[str]				=			{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Dict				=			[
      """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """WavLMForAudioFrameClassification""",
      """WavLMForCTC""",
      """WavLMForSequenceClassification""",
      """WavLMForXVector""",
      """WavLMModel""",
      """WavLMPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  _A					:				List[Any]				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
