code: 
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  UpperCAmelCase			:			str	  = {
      """configuration_switch_transformers""": [
          """SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
          """SwitchTransformersConfig""",
          """SwitchTransformersOnnxConfig""",
      ]
  }
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCAmelCase			:			Any	  = [
           """SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """SwitchTransformersEncoderModel""",
           """SwitchTransformersForConditionalGeneration""",
           """SwitchTransformersModel""",
           """SwitchTransformersPreTrainedModel""",
           """SwitchTransformersTop1Router""",
           """SwitchTransformersSparseMLP""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_switch_transformers import (
           SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
           SwitchTransformersConfig,
           SwitchTransformersOnnxConfig,
       )
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_switch_transformers import (
                SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
                SwitchTransformersEncoderModel,
                SwitchTransformersForConditionalGeneration,
                SwitchTransformersModel,
                SwitchTransformersPreTrainedModel,
                SwitchTransformersSparseMLP,
                SwitchTransformersTopaRouter,
            )
  
  
  else:
       import sys
  
       UpperCAmelCase			:			Union[str, Any]	  = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import importlib
  import sys
  from argparse import REMAINDER, ArgumentParser
  from pathlib import Path
  
  import torch_xla.distributed.xla_multiprocessing as xmp
  def   _A (	):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Optional[int]            =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   _A (	):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Optional[Any]            =parse_args()
  
    # Import training_script as a module.
    a__			:      Tuple            =Path(args.training_script		)
    sys.path.append(str(script_fpath.parent.resolve()		)		)
    a__			:      Tuple            =script_fpath.stem
    a__			:      Any            =importlib.import_module(SCREAMING_SNAKE_CASE		)
  
    # Patch sys.argv
    a__			:      List[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()
  
  
  
  
  
  
  