from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase			:							Optional[int] ={"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase			:							Union[str, Any] =[
        """VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ViTMAEForPreTraining""",
        """ViTMAELayer""",
        """ViTMAEModel""",
        """ViTMAEPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase			:							str =[
        """TFViTMAEForPreTraining""",
        """TFViTMAEModel""",
        """TFViTMAEPreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vit_mae import (
            VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
            ViTMAEForPreTraining,
            ViTMAELayer,
            ViTMAEModel,
            ViTMAEPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
    import sys

    lowerCAmelCase			:							int =_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__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		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=__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 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						parse_args()

    # Import training_script as a module.
    _lowerCamelCase     :		List[Any] 						=						Path(args.training_script	)
    sys.path.append(str(script_fpath.parent.resolve()	)	)
    _lowerCamelCase     :		Optional[Any] 						=						script_fpath.stem
    _lowerCamelCase     :		Dict 						=						importlib.import_module(__A	)

    # Patch sys.argv
    _lowerCamelCase     :		Union[str, 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()




