

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

import torch_xla.distributed.xla_multiprocessing as xmp



def   UpperCamelCase(   )      ->							List[str]:





  '''simple docstring'''





  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=_A     ,   default=1     ,   help="""Number of TPU cores to use (1 or 8)."""				)

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

  return parser.parse_args()



def   UpperCamelCase(   )      ->							Dict:





  '''simple docstring'''





  snake_case_					= parse_args()

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

  # Patch sys.argv
  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 List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
  import torch

UpperCAmelCase__			:  int					     =     logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
  from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






  def        __lowercase   (  cls      )  ->    Any:
        return cls






def        __lowercase   (  _A    ,     _A = None    ,     _A = None    ,     _A = None    ,     _A = None    ,     )  ->    Tuple:
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = device
      SCREAMING_SNAKE_CASE			:   Tuple	  = shape[0]

      SCREAMING_SNAKE_CASE			:   Optional[int]	  = layout or torch.strided
      SCREAMING_SNAKE_CASE			:   Dict	  = device or torch.device("""cpu"""      )

      if generator is not None:
            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = generator.device.type if not isinstance(_A    ,     _A      ) else generator[0].device.type
            if gen_device_type != device.type and gen_device_type == "cpu":
                  SCREAMING_SNAKE_CASE			:   Tuple	  = """cpu"""
                  if device != "mps":
                        logger.info(
                            F"The passed generator was created on 'cpu' even though a tensor on {device} was expected."
                            F" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"
                            F" slighly speed up this function by passing a generator that was created on the {device} device."      )
            elif gen_device_type != device.type and gen_device_type == "cuda":
                  raise ValueError(F"Cannot generate a {device} tensor from a generator of type {gen_device_type}."      )

      if isinstance(_A    ,     _A      ):
            SCREAMING_SNAKE_CASE			:   int	  = (1,) + shape[1:]
            SCREAMING_SNAKE_CASE			:   Any	  = [
                torch.randn(_A    ,     generator=generator[i]    ,     device=_A    ,     dtype=_A    ,     layout=_A      )
                for i in range(_A      )
            ]
            SCREAMING_SNAKE_CASE			:   Any	  = torch.cat(_A    ,     dim=0      ).to(_A      )
      else:
            SCREAMING_SNAKE_CASE			:   int	  = torch.randn(_A    ,     generator=_A    ,     device=_A    ,     dtype=_A    ,     layout=_A      ).to(_A      )

      return latents







def        __lowercase   (  _A      )  ->    Dict:
      if is_torch_version("""<"""    ,     """2.0.0"""      ) or not hasattr(_A    ,     """_dynamo"""      ):
            return False
      return isinstance(_A    ,     torch._dynamo.eval_frame.OptimizedModule      )




