

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

import torch_xla.distributed.xla_multiprocessing as xmp







def 						lowerCamelCase__      ( ):


       """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 						lowerCamelCase__      ( ):


       """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 TYPE_CHECKING

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


UpperCamelCase__		 =     {
    '''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     ['''ConvNextFeatureExtractor''']
    UpperCamelCase__		 =     ['''ConvNextImageProcessor''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     [
        '''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ConvNextForImageClassification''',
        '''ConvNextModel''',
        '''ConvNextPreTrainedModel''',
        '''ConvNextBackbone''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     [
        '''TFConvNextForImageClassification''',
        '''TFConvNextModel''',
        '''TFConvNextPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_convnext import ConvNextFeatureExtractor
        from .image_processing_convnext import ConvNextImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convnext import (
            CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvNextBackbone,
            ConvNextForImageClassification,
            ConvNextModel,
            ConvNextPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
    import sys

    UpperCamelCase__		 =     _LazyModule(__name__, globals()['''__file__'''], _import_structure)

