



"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def       _snake_case						(						):
 UpperCAmelCase	:					Union[str, 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       _snake_case						(						):
 UpperCAmelCase	:					List[str]	=	parse_args()

 # Import training_script as a module.
 UpperCAmelCase	:					int	=	Path(args.training_script						)
 sys.path.append(str(script_fpath.parent.resolve()						)						)
 UpperCAmelCase	:					Union[str, Any]	=	script_fpath.stem
 UpperCAmelCase	:					Any	=	importlib.import_module(UpperCamelCase						)

 # Patch sys.argv
 UpperCAmelCase	:					Dict	=	[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()





"""simple docstring"""
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


A:   List[str]								=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Tuple								=		["DPTFeatureExtractor"]
      A:   int								=		["DPTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[Any]								=		[
          "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPTForDepthEstimation",
          "DPTForSemanticSegmentation",
          "DPTModel",
          "DPTPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_dpt import DPTFeatureExtractor
            from .image_processing_dpt import DPTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dpt import (
                DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPTForDepthEstimation,
                DPTForSemanticSegmentation,
                DPTModel,
                DPTPreTrainedModel,
            )


else:
      import sys

      A:   str								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
