






"""simple docstring"""



from typing import TYPE_CHECKING

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


a  :						Optional[Any]					       =							{
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :						Dict					       =							[
     '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''FlaubertForMultipleChoice''',
     '''FlaubertForQuestionAnswering''',
     '''FlaubertForQuestionAnsweringSimple''',
     '''FlaubertForSequenceClassification''',
     '''FlaubertForTokenClassification''',
     '''FlaubertModel''',
     '''FlaubertWithLMHeadModel''',
     '''FlaubertPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :						str					       =							[
     '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFFlaubertForMultipleChoice''',
     '''TFFlaubertForQuestionAnsweringSimple''',
     '''TFFlaubertForSequenceClassification''',
     '''TFFlaubertForTokenClassification''',
     '''TFFlaubertModel''',
     '''TFFlaubertPreTrainedModel''',
     '''TFFlaubertWithLMHeadModel''',
 ]


if TYPE_CHECKING:
 from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
 from .tokenization_flaubert import FlaubertTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flaubert import (
      FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaubertForMultipleChoice,
      FlaubertForQuestionAnswering,
      FlaubertForQuestionAnsweringSimple,
      FlaubertForSequenceClassification,
      FlaubertForTokenClassification,
      FlaubertModel,
      FlaubertPreTrainedModel,
      FlaubertWithLMHeadModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_flaubert import (
      TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFFlaubertForMultipleChoice,
      TFFlaubertForQuestionAnsweringSimple,
      TFFlaubertForSequenceClassification,
      TFFlaubertForTokenClassification,
      TFFlaubertModel,
      TFFlaubertPreTrainedModel,
      TFFlaubertWithLMHeadModel,
  )

else:
 import sys

 a  :						Union[str, Any]					       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)











"""simple docstring"""



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


a  :						Dict					       =							logging.getLogger(__name__)




def       snake_case__		(   )    ->Any:
 UpperCAmelCase__ 						=				ArgumentParser()
 UpperCAmelCase__						,	UpperCAmelCase__ 						=				parser.parse_known_args()
 for arg in unknown:
  if arg.startswith(("""-""", """--""")			):
   parser.add_argument(arg.split("""="""			)[0]			)

 return parser.parse_args()




def       snake_case__		(   )    ->List[Any]:
 UpperCAmelCase__ 						=				parse_args()
 UpperCAmelCase__ 						=				8_8_8_8
 UpperCAmelCase__ 						=				int(os.environ["""SM_NUM_GPUS"""]			)
 UpperCAmelCase__ 						=				json.loads(os.environ["""SM_HOSTS"""]			)
 UpperCAmelCase__ 						=				len(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				os.environ["""SM_CURRENT_HOST"""]
 UpperCAmelCase__ 						=				hosts.index(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				"""INFO"""

 if num_nodes > 1:
  UpperCAmelCase__ 						=				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__ 						=				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(_SCREAMING_SNAKE_CASE       ,	shell=_SCREAMING_SNAKE_CASE			)
 except Exception as e:
  logger.info(_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
 main()



