



"""simple docstring"""






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

import torch_xla.distributed.xla_multiprocessing as xmp


def     _lowercase       (						)		->     int:
			__lowerCAmelCase   :						Union[str, Any]				= 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     _lowercase       (						)		->     str:
			__lowerCAmelCase   :						int				= parse_args()

			# Import training_script as a module.
			__lowerCAmelCase   :						Union[str, Any]				= Path(args.training_script			)
			sys.path.append(str(script_fpath.parent.resolve()			)			)
			__lowerCAmelCase   :						Any				= script_fpath.stem
			__lowerCAmelCase   :						List[Any]				= importlib.import_module(__snake_case			)

			# Patch sys.argv
			__lowerCAmelCase   :						str				= [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()




"""simple docstring"""






import logging

from transformers.configuration_utils import PretrainedConfig


__snake_case							:     Tuple   =					logging.getLogger(__name__)



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'masked_bert'

	def __init__(  self:	str ,    _SCREAMING_SNAKE_CASE:	Tuple=3_0522 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=768 ,    _SCREAMING_SNAKE_CASE:	List[str]=12 ,    _SCREAMING_SNAKE_CASE:	Any=12 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=3072 ,    _SCREAMING_SNAKE_CASE:	List[str]="gelu" ,    _SCREAMING_SNAKE_CASE:	List[str]=0.1 ,    _SCREAMING_SNAKE_CASE:	str=0.1 ,    _SCREAMING_SNAKE_CASE:	Dict=512 ,    _SCREAMING_SNAKE_CASE:	List[Any]=2 ,    _SCREAMING_SNAKE_CASE:	Tuple=0.02 ,    _SCREAMING_SNAKE_CASE:	int=1e-12 ,    _SCREAMING_SNAKE_CASE:	int=0 ,    _SCREAMING_SNAKE_CASE:	Tuple="topK" ,    _SCREAMING_SNAKE_CASE:	Any="constant" ,    _SCREAMING_SNAKE_CASE:	int=0.0 ,    **_SCREAMING_SNAKE_CASE:	Any ,    )       -> List[str]:

				"""simple docstring"""

				super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Union[str, Any]				= vocab_size
				__lowerCAmelCase   :						List[str]				= hidden_size
				__lowerCAmelCase   :						Any				= num_hidden_layers
				__lowerCAmelCase   :						Dict				= num_attention_heads
				__lowerCAmelCase   :						Union[str, Any]				= hidden_act
				__lowerCAmelCase   :						Optional[Any]				= intermediate_size
				__lowerCAmelCase   :						int				= hidden_dropout_prob
				__lowerCAmelCase   :						List[Any]				= attention_probs_dropout_prob
				__lowerCAmelCase   :						int				= max_position_embeddings
				__lowerCAmelCase   :						str				= type_vocab_size
				__lowerCAmelCase   :						Any				= initializer_range
				__lowerCAmelCase   :						Optional[int]				= layer_norm_eps
				__lowerCAmelCase   :						Tuple				= pruning_method
				__lowerCAmelCase   :						Union[str, Any]				= mask_init
				__lowerCAmelCase   :						str				= mask_scale