



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A:   Optional[Any]								=		{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[int]								=		[
          "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TapasForMaskedLM",
          "TapasForQuestionAnswering",
          "TapasForSequenceClassification",
          "TapasModel",
          "TapasPreTrainedModel",
          "load_tf_weights_in_tapas",
      ]
try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   List[str]								=		[
          "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFTapasForMaskedLM",
          "TFTapasForQuestionAnswering",
          "TFTapasForSequenceClassification",
          "TFTapasModel",
          "TFTapasPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
      from .tokenization_tapas import TapasTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tapas import (
                TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TapasForMaskedLM,
                TapasForQuestionAnswering,
                TapasForSequenceClassification,
                TapasModel,
                TapasPreTrainedModel,
                load_tf_weights_in_tapas,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_tapas import (
                TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTapasForMaskedLM,
                TFTapasForQuestionAnswering,
                TFTapasForSequenceClassification,
                TFTapasModel,
                TFTapasPreTrainedModel,
            )


else:
      import sys

      A:   Optional[Any]								=		_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 			UpperCamelCase(  ):
		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=__UpperCamelCase     ,default=1     ,help='''Number of TPU cores to use (1 or 8).''' )

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

		return parser.parse_args()


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     str									= parse_args()

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

		# Patch sys.argv
		lowerCAmelCase_			:     Optional[int]									= [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()





