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

import torch_xla.distributed.xla_multiprocessing as xmp







def        A__					(						)  ->	Any:
				UpperCamelCase_:							Optional[Any]	=						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]:
				UpperCamelCase_:							int	=						parse_args()

				# Import training_script as a module.
				UpperCamelCase_:							Tuple	=						Path(args.training_script						)
				sys.path.append(str(script_fpath.parent.resolve()						)						)
				UpperCamelCase_:							Optional[Any]	=						script_fpath.stem
				UpperCamelCase_:							int	=						importlib.import_module(lowerCamelCase_						)

				# Patch sys.argv
				UpperCamelCase_:							str	=						[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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_						: List[str]         =  {
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Optional[Any]         =  [
							    """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MgpstrModel""",
							    """MgpstrPreTrainedModel""",
							    """MgpstrForSceneTextRecognition""",
							]

if TYPE_CHECKING:
							from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
							from .processing_mgp_str import MgpstrProcessor
							from .tokenization_mgp_str import MgpstrTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mgp_str import (
														    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MgpstrForSceneTextRecognition,
														    MgpstrModel,
														    MgpstrPreTrainedModel,
														)
else:
							import sys

							lowerCamelCase_						: int         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






