


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

import torch_xla.distributed.xla_multiprocessing as xmp






def 						_A						(						)			->				Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase				=	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=lowercase_			,       default=1			,       help='Number of TPU cores to use (1 or 8).'				)

     # positional
     parser.add_argument(
         'training_script'			,       type=lowercase_			,       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=lowercase_				)

     return parser.parse_args()






def 						_A						(						)			->				str:


     """simple docstring"""


     __UpperCamelCase				=	parse_args()

     # Import training_script as a module.
     __UpperCamelCase				=	Path(args.training_script				)
     sys.path.append(str(script_fpath.parent.resolve()				)				)
     __UpperCamelCase				=	script_fpath.stem
     __UpperCamelCase				=	importlib.import_module(lowercase_				)

     # Patch sys.argv
     __UpperCamelCase				=	[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()






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__snake_case     =		{
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case     =		[
        '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ConvNextV2ForImageClassification''',
        '''ConvNextV2Model''',
        '''ConvNextV2PreTrainedModel''',
        '''ConvNextV2Backbone''',
    ]


if TYPE_CHECKING:
    from .configuration_convnextva import (
        CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
        ConvNextVaConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convnextva import (
            CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvNextVaBackbone,
            ConvNextVaForImageClassification,
            ConvNextVaModel,
            ConvNextVaPreTrainedModel,
        )

else:
    import sys

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


