




import json
import logging
import os
import subprocess
from argparse import ArgumentParser


UpperCAmelCase_   							=      logging.getLogger(__name__)




def    lowerCamelCase__			(		):





       '''simple docstring'''



       __lowerCamelCase  =		ArgumentParser()
       __lowerCamelCase,  __lowerCamelCase  =		parser.parse_known_args()
       for arg in unknown:
              if arg.startswith(("""-""", """--""")      ):
                     parser.add_argument(arg.split("""="""      )[0]      )

       return parser.parse_args()




def    lowerCamelCase__			(		):





       '''simple docstring'''



       __lowerCamelCase  =		parse_args()
       __lowerCamelCase  =		8888
       __lowerCamelCase  =		int(os.environ["""SM_NUM_GPUS"""]      )
       __lowerCamelCase  =		json.loads(os.environ["""SM_HOSTS"""]      )
       __lowerCamelCase  =		len(A__      )
       __lowerCamelCase  =		os.environ["""SM_CURRENT_HOST"""]
       __lowerCamelCase  =		hosts.index(A__      )
       __lowerCamelCase  =		"""INFO"""

       if num_nodes > 1:
              __lowerCamelCase  =		f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]      )}'
       else:
              __lowerCamelCase  =		f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]      )}'
       try:
              subprocess.run(A__    ,     shell=A__      )
       except Exception as e:
              logger.info(A__      )


if __name__ == "__main__":
       main()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_   							=      {'configuration_dinat': ['DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DinatConfig']}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_   							=      [
           'DINAT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'DinatForImageClassification',
           'DinatModel',
           'DinatPreTrainedModel',
           'DinatBackbone',
       ]

if TYPE_CHECKING:
       from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_dinat import (
                  DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DinatBackbone,
                  DinatForImageClassification,
                  DinatModel,
                  DinatPreTrainedModel,
              )

else:
       import sys

       UpperCAmelCase_   							=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

