





"""simple docstring"""
import json
import logging
import os
import subprocess
from argparse import ArgumentParser


lowerCAmelCase:	Optional[int]							      =logging.getLogger(__name__)




def 			__snake_case						(      )		->				List[str]:
   lowercase    :			Union[str, Any]  =       ArgumentParser()
   lowercase    :			Optional[int]  =       parser.parse_known_args()
   for arg in unknown:
      if arg.startswith(("""-""", """--""")    ):
         parser.add_argument(arg.split("""="""    )[0]    )

   return parser.parse_args()




def 			__snake_case						(      )		->				Optional[Any]:
   lowercase    :			int  =       parse_args()
   lowercase    :			Dict  =       8888
   lowercase    :			Union[str, Any]  =       int(os.environ["""SM_NUM_GPUS"""]    )
   lowercase    :			Optional[Any]  =       json.loads(os.environ["""SM_HOSTS"""]    )
   lowercase    :			Dict  =       len(snake_case__    )
   lowercase    :			int  =       os.environ['SM_CURRENT_HOST']
   lowercase    :			List[str]  =       hosts.index(snake_case__    )
   lowercase    :			Optional[Any]  =       'INFO'

   if num_nodes > 1:
      lowercase    :			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:
      lowercase    :			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()









"""simple docstring"""
import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
   import transformer_engine.pytorch as te




def 			__snake_case						(      __A ,__A=True ,__A=True ,__A=True    )		->				Union[str, Any]:
   if not is_fpa_available():
      raise ImportError("""Using `convert_model` requires transformer_engine to be installed."""    )
   for name, module in model.named_children():
      if isinstance(__A ,nn.Linear    ) and to_transformer_engine and _convert_linear:
         # Return early if the linear layer weights are not multiples of 16
         if any(p % 16 != 0 for p in module.weight.shape    ):
            return
         lowercase    :			Optional[int]  =       module.bias is not None
         lowercase    :			Union[str, Any]  =       te.Linear(
             module.in_features ,module.out_features ,bias=__A ,params_dtype=module.weight.dtype    )
         lowercase    :			Union[str, Any]  =       module.weight.data.clone()
         if has_bias:
            lowercase    :			Dict  =       module.bias.data.clone()

         setattr(__A ,__A ,__A    )
      elif isinstance(__A ,nn.LayerNorm    ) and to_transformer_engine and _convert_ln:
         lowercase    :			List[str]  =       te.LayerNorm(module.normalized_shape[0] ,eps=module.eps ,params_dtype=module.weight.dtype    )
         lowercase    :			Optional[Any]  =       module.weight.data.clone()
         lowercase    :			Any  =       module.bias.data.clone()

         setattr(__A ,__A ,__A    )
      elif isinstance(__A ,te.Linear    ) and not to_transformer_engine and _convert_linear:
         lowercase    :			Any  =       module.bias is not None
         lowercase    :			List[Any]  =       nn.Linear(
             module.in_features ,module.out_features ,bias=__A ,params_dtype=module.weight.dtype    )
         lowercase    :			Union[str, Any]  =       module.weight.data.clone()
         if has_bias:
            lowercase    :			Optional[Any]  =       module.bias.data.clone()

         setattr(__A ,__A ,__A    )
      elif isinstance(__A ,te.LayerNorm    ) and not to_transformer_engine and _convert_ln:
         lowercase    :			Any  =       nn.LayerNorm(module.normalized_shape[0] ,eps=module.eps ,params_dtype=module.weight.dtype    )
         lowercase    :			str  =       module.weight.data.clone()
         lowercase    :			List[str]  =       module.bias.data.clone()

         setattr(__A ,__A ,__A    )
      else:
         convert_model(
             __A ,to_transformer_engine=__A ,_convert_linear=_convert_linear ,_convert_ln=_convert_ln ,)







def 			__snake_case						(      __A    )		->				Union[str, Any]:
   if not is_fpa_available():
      raise ImportError("""Using `has_transformer_engine_layers` requires transformer_engine to be installed."""    )
   for m in model.modules():
      if isinstance(__A ,(te.LayerNorm, te.Linear)    ):
         return True
   return False


