

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

import torch_xla.distributed.xla_multiprocessing as xmp






def        SCREAMING_SNAKE_CASE__	(  )  ->					Dict:
	__lowerCamelCase    :    List[str] 	=			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        SCREAMING_SNAKE_CASE__	(  )  ->					Union[str, Any]:
	__lowerCamelCase    :    List[str] 	=			parse_args()

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

	# Patch sys.argv
	__lowerCamelCase    :    Optional[Any] 	=			[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, is_vision_available


a		={
    """configuration_pix2struct""": [
        """PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Pix2StructConfig""",
        """Pix2StructTextConfig""",
        """Pix2StructVisionConfig""",
    ],
    """processing_pix2struct""": ["""Pix2StructProcessor"""],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""Pix2StructImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """Pix2StructPreTrainedModel""",
					    """Pix2StructForConditionalGeneration""",
					    """Pix2StructVisionModel""",
					    """Pix2StructTextModel""",
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




