


'''simple docstring'''



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

import torch_xla.distributed.xla_multiprocessing as xmp






def   lowerCamelCase							():
					__a      :	Optional[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=_SCREAMING_SNAKE_CASE						,				default=1						,				help='Number of TPU cores to use (1 or 8).'       )

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

					return parser.parse_args()






def   lowerCamelCase							():
					__a      :	Optional[Any]    =  parse_args()

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

					# Patch sys.argv
					__a      :	List[Any]    =  [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'''



from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


__lowercase     :						List[Any]	=     {
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   "ernie_m"
	A_           =   {"dropout": "classifier_dropout", "num_classes": "num_labels"}




	def __init__(       self						,    __a = 25_0002						,    __a = 768						,    __a = 12						,    __a = 12						,    __a = 3072						,    __a = "gelu"						,    __a = 0.1						,    __a = 0.1						,    __a = 514						,    __a = 0.02						,    __a = 1						,    __a = 1E-0_5						,    __a=None						,    __a=False						,    __a=0.0						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(pad_token_id=__a						,    **__a							)
						__a      :	int    =  vocab_size
						__a      :	Dict    =  hidden_size
						__a      :	str    =  num_hidden_layers
						__a      :	Dict    =  num_attention_heads
						__a      :	List[str]    =  intermediate_size
						__a      :	Union[str, Any]    =  hidden_act
						__a      :	List[Any]    =  hidden_dropout_prob
						__a      :	str    =  attention_probs_dropout_prob
						__a      :	Any    =  max_position_embeddings
						__a      :	int    =  initializer_range
						__a      :	Dict    =  layer_norm_eps
						__a      :	int    =  classifier_dropout
						__a      :	Dict    =  is_decoder
						__a      :	int    =  act_dropout



