




'''simple docstring'''





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


a		:	Tuple       =      logging.getLogger(__name__)





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

    return parser.parse_args()





def UpperCAmelCase    (       ):
    _a				:							List[str]							       =      parse_args()
    _a				:							Optional[int]							       =      8_888
    _a				:							str							       =      int(os.environ["""SM_NUM_GPUS"""]						)
    _a				:							Optional[Any]							       =      json.loads(os.environ["""SM_HOSTS"""]						)
    _a				:							Optional[Any]							       =      len(UpperCAmelCase_						)
    _a				:							Union[str, Any]							       =      os.environ["""SM_CURRENT_HOST"""]
    _a				:							Dict							       =      hosts.index(UpperCAmelCase_						)
    _a				:							List[str]							       =      """INFO"""

    if num_nodes > 1:
        _a				:							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:
        _a				:							Tuple							       =      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(UpperCAmelCase_   ,	shell=UpperCAmelCase_						)
    except Exception as e:
        logger.info(UpperCAmelCase_						)


if __name__ == "__main__":
  main()





from typing import TYPE_CHECKING

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


__A     						=      {'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''HubertForCTC''',
     '''HubertForSequenceClassification''',
     '''HubertModel''',
     '''HubertPreTrainedModel''',
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFHubertForCTC''',
     '''TFHubertModel''',
     '''TFHubertPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_hubert import (
      HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      HubertForCTC,
      HubertForSequenceClassification,
      HubertModel,
      HubertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_hubert import (
      TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFHubertForCTC,
      TFHubertModel,
      TFHubertPreTrainedModel,
  )


else:
 import sys

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

