




from typing import TYPE_CHECKING

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


__UpperCamelCase					:       List[Any]    					=      {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       Any    					=      ["DPTFeatureExtractor"]
		__UpperCamelCase					:       Union[str, Any]    					=      ["DPTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       Tuple    					=      [
		    "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

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







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()

