




import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def   lowerCAmelCase						(						):




 """simple docstring"""







 UpperCAmelCase__								=  ArgumentParser(
     description=(
         "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
     )		)

 # Optional arguments for the launch helper
 parser.add_argument("--num_cores"  ,							type=lowercase__  ,							default=1  ,							help="Number of TPU cores to use (1 or 8)."		)

 # positional
 parser.add_argument(
     "training_script"  ,							type=lowercase__  ,							help=(
         "The full path to the single TPU training "
         "program/script to be launched in parallel, "
         "followed by all the arguments for the "
         "training script"
     )  ,							)

 # rest from the training program
 parser.add_argument("training_script_args"  ,							nargs=lowercase__		)

 return parser.parse_args()



def   lowerCAmelCase						(						):




 """simple docstring"""







 UpperCAmelCase__								=  parse_args()

 # Import training_script as a module.
 UpperCAmelCase__								=  Path(args.training_script		)
 sys.path.append(str(script_fpath.parent.resolve()		)		)
 UpperCAmelCase__								=  script_fpath.stem
 UpperCAmelCase__								=  importlib.import_module(lowercase__		)

 # Patch sys.argv
 UpperCAmelCase__								=  [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores		)]

 xmp.spawn(mod._mp_fn  ,							args=()  ,							nprocs=args.num_cores		)


if __name__ == "__main__":
  main()






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase  :				Dict						=				logging.get_logger(__name__)

_lowerCAmelCase  :				Optional[int]						=				{
    "google/rembert": "https://huggingface.co/google/rembert/resolve/main/config.json",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}
class      _UpperCamelCase (			lowerCAmelCase						):
      UpperCAmelCase_            =						"""rembert"""
      def __init__(     self				:Union[str, Any]	, lowerCamelCase				:Union[str, Any]=25_0300	, lowerCamelCase				:Optional[Any]=1152	, lowerCamelCase				:Optional[int]=32	, lowerCamelCase				:Union[str, Any]=18	, lowerCamelCase				:str=256	, lowerCamelCase				:Any=1664	, lowerCamelCase				:Any=4608	, lowerCamelCase				:List[str]="gelu"	, lowerCamelCase				:Optional[Any]=0.0	, lowerCamelCase				:Optional[int]=0.0	, lowerCamelCase				:Dict=0.1	, lowerCamelCase				:List[str]=512	, lowerCamelCase				:int=2	, lowerCamelCase				:str=0.02	, lowerCamelCase				:Optional[int]=1e-12	, lowerCamelCase				:List[Any]=True	, lowerCamelCase				:Optional[Any]=0	, lowerCamelCase				:Optional[Any]=312	, lowerCamelCase				:int=313	, **lowerCamelCase				:Dict	, )       ->     Any:
       super().__init__(pad_token_id=lowerCamelCase	, bos_token_id=lowerCamelCase	, eos_token_id=lowerCamelCase	, **lowerCamelCase )

       UpperCAmelCase__								=  vocab_size
       UpperCAmelCase__								=  input_embedding_size
       UpperCAmelCase__								=  output_embedding_size
       UpperCAmelCase__								=  max_position_embeddings
       UpperCAmelCase__								=  hidden_size
       UpperCAmelCase__								=  num_hidden_layers
       UpperCAmelCase__								=  num_attention_heads
       UpperCAmelCase__								=  intermediate_size
       UpperCAmelCase__								=  hidden_act
       UpperCAmelCase__								=  hidden_dropout_prob
       UpperCAmelCase__								=  attention_probs_dropout_prob
       UpperCAmelCase__								=  classifier_dropout_prob
       UpperCAmelCase__								=  initializer_range
       UpperCAmelCase__								=  type_vocab_size
       UpperCAmelCase__								=  layer_norm_eps
       UpperCAmelCase__								=  use_cache
       UpperCAmelCase__								=  False



class      _UpperCamelCase (			lowerCAmelCase						):
      @property
      def 		UpperCAmelCase_  (     self				:List[Any] )       ->     Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
        UpperCAmelCase__								=  {0: "batch", 1: "choice", 2: "sequence"}
       else:
        UpperCAmelCase__								=  {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("token_type_ids", dynamic_axis),
           ] )







      @property
      def 		UpperCAmelCase_  (     self				:Union[str, Any] )       ->     float:
       return 1e-4





