code: 
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  __UpperCAmelCase      = {
      '''configuration_donut_swin''': ['''DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DonutSwinConfig'''],
      '''processing_donut''': ['''DonutProcessor'''],
  }
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      __UpperCAmelCase      = [
          '''DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DonutSwinModel''',
          '''DonutSwinPreTrainedModel''',
      ]
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      __UpperCAmelCase      = ['''DonutFeatureExtractor''']
      __UpperCAmelCase      = ['''DonutImageProcessor''']
  
  
  if TYPE_CHECKING:
      from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
      from .processing_donut import DonutProcessor
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_donut_swin import (
              DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
              DonutSwinModel,
              DonutSwinPreTrainedModel,
          )
  
      try:
          if not is_vision_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .feature_extraction_donut import DonutFeatureExtractor
          from .image_processing_donut import DonutImageProcessor
  
  else:
      import sys
  
      __UpperCAmelCase      = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
style_context: 
  
  
  
  import json
  import logging
  import os
  import subprocess
  from argparse import ArgumentParser
  
  
  __UpperCAmelCase      = logging.getLogger(__name__)
  def 	UpperCamelCase			(     )			->  List[str]:
     UpperCamelCase					:						Union[str, Any]					=			ArgumentParser()
     UpperCamelCase			,  UpperCamelCase					:						Optional[int]					=			parser.parse_known_args()
     for arg in unknown:
        if arg.startswith(('-', '--')			):
           parser.add_argument(arg.split('='			)[0]			)
  
     return parser.parse_args()
  def 	UpperCamelCase			(     )			->  Optional[Any]:
     UpperCamelCase					:						int					=			parse_args()
     UpperCamelCase					:						Dict					=			8888
     UpperCamelCase					:						Union[str, Any]					=			int(os.environ['SM_NUM_GPUS']			)
     UpperCamelCase					:						Optional[Any]					=			json.loads(os.environ['SM_HOSTS']			)
     UpperCamelCase					:						Dict					=			len(snake_case__			)
     UpperCamelCase					:						int					=			os.environ['SM_CURRENT_HOST']
     UpperCamelCase					:						List[str]					=			hosts.index(snake_case__			)
     UpperCamelCase					:						Optional[Any]					=			'INFO'
  
     if num_nodes > 1:
        UpperCamelCase					:						List[Any]					=			F"""python -m torch.distributed.launch \
                  --nnodes={num_nodes}  \
                  --node_rank={rank}  \
                  --nproc_per_node={num_gpus}  \
                  --master_addr={hosts[0]}  \
                  --master_port={port} \
                  ./run_glue.py \
                  {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}"""
     else:
        UpperCamelCase					:						Any					=			F"""python -m torch.distributed.launch \
              --nproc_per_node={num_gpus}  \
              ./run_glue.py \
              {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}"""
     try:
        subprocess.run(snake_case__ ,  shell=snake_case__			)
     except Exception as e:
        logger.info(snake_case__			)
  
  
  if __name__ == "__main__":
      main()
  
  
  
  