


'''simple docstring'''



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'''






     __UpperCAmelCase			:				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=_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			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				Union[str, Any]													=    parse_args()

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

     # Patch sys.argv
     __UpperCAmelCase			:				List[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()



'''simple docstring'''



from typing import TYPE_CHECKING

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


__UpperCAmelCase    :Optional[Any]				       =     {
    "configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
    "processing_layoutlmv2": ["LayoutLMv2Processor"],
    "tokenization_layoutlmv2": ["LayoutLMv2Tokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    :Any				       =     ["LayoutLMv2TokenizerFast"]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    :int				       =     ["LayoutLMv2FeatureExtractor"]
      __UpperCAmelCase    :Optional[int]				       =     ["LayoutLMv2ImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase    :List[Any]				       =     [
          "LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST",
          "LayoutLMv2ForQuestionAnswering",
          "LayoutLMv2ForSequenceClassification",
          "LayoutLMv2ForTokenClassification",
          "LayoutLMv2Layer",
          "LayoutLMv2Model",
          "LayoutLMv2PreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
      from .processing_layoutlmva import LayoutLMvaProcessor
      from .tokenization_layoutlmva import LayoutLMvaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaLayer,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )
else:
      import sys

      __UpperCAmelCase    :Any				       =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)