




"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase__						=							logging.get_logger(__name__)

UpperCamelCase__						=							{
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}




class   a__			(       UpperCamelCase_  ):
						snake_case__             = 'roberta-prelayernorm'





						def __init__(							self      :	List[Any]     ,a__      :	int=5_0265     ,a__      :	Any=768     ,a__      :	str=12     ,a__      :	List[str]=12     ,a__      :	Any=3072     ,a__      :	List[Any]="gelu"     ,a__      :	Tuple=0.1     ,a__      :	int=0.1     ,a__      :	Dict=512     ,a__      :	List[Any]=2     ,a__      :	str=0.02     ,a__      :	str=1E-12     ,a__      :	List[Any]=1     ,a__      :	Optional[int]=0     ,a__      :	Optional[Any]=2     ,a__      :	Any="absolute"     ,a__      :	Optional[Any]=True     ,a__      :	Any=None     ,**a__      :	Union[str, Any]     ,)						->       List[Any]:




											"""simple docstring"""







											super().__init__(pad_token_id=a__     ,bos_token_id=a__     ,eos_token_id=a__     ,**a__)

											_lowerCAmelCase:Union[str, Any]									=  vocab_size
											_lowerCAmelCase:List[str]									=  hidden_size
											_lowerCAmelCase:Optional[Any]									=  num_hidden_layers
											_lowerCAmelCase:Optional[Any]									=  num_attention_heads
											_lowerCAmelCase:Tuple									=  hidden_act
											_lowerCAmelCase:Union[str, Any]									=  intermediate_size
											_lowerCAmelCase:Optional[Any]									=  hidden_dropout_prob
											_lowerCAmelCase:int									=  attention_probs_dropout_prob
											_lowerCAmelCase:int									=  max_position_embeddings
											_lowerCAmelCase:Optional[Any]									=  type_vocab_size
											_lowerCAmelCase:Tuple									=  initializer_range
											_lowerCAmelCase:List[Any]									=  layer_norm_eps
											_lowerCAmelCase:Union[str, Any]									=  position_embedding_type
											_lowerCAmelCase:Tuple									=  use_cache
											_lowerCAmelCase:Optional[int]									=  classifier_dropout




class   a__			(       UpperCamelCase_  ):





						@property
						def       __UpperCamelCase     (							self      :	Dict)						->       Mapping[str, Mapping[int, str]]:




											"""simple docstring"""







											if self.task == "multiple-choice":
																_lowerCAmelCase:Optional[int]									=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
											else:
																_lowerCAmelCase:Union[str, Any]									=  {0: '''batch''', 1: '''sequence'''}
											return OrderedDict(
											    [
											        ('''input_ids''', dynamic_axis),
											        ('''attention_mask''', dynamic_axis),
											    ])












"""simple docstring"""






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

import torch_xla.distributed.xla_multiprocessing as xmp





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Optional[int]									=  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=snake_case      ,       default=1      ,       help='''Number of TPU cores to use (1 or 8).'''    )

					# positional
					parser.add_argument(
					    '''training_script'''      ,       type=snake_case      ,       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=snake_case    )

					return parser.parse_args()





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:int									=  parse_args()

					# Import training_script as a module.
					_lowerCAmelCase:Optional[int]									=  Path(args.training_script    )
					sys.path.append(str(script_fpath.parent.resolve()    )    )
					_lowerCAmelCase:Optional[int]									=  script_fpath.stem
					_lowerCAmelCase:Any									=  importlib.import_module(snake_case    )

					# Patch sys.argv
					_lowerCAmelCase:Dict									=  [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()






