from collections import OrderedDict
from typing import Mapping

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


__lowercase										= logging.get_logger(__name__)

__lowercase										= {
    '''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large-mnli''': (
        '''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
    ),
}


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						List[str]      =				"""ibert"""







    def __init__(     self			,			__lowercase=30_522			,			__lowercase=768			,			__lowercase=12			,			__lowercase=12			,			__lowercase=3_072			,			__lowercase="gelu"			,			__lowercase=0.1			,			__lowercase=0.1			,			__lowercase=512			,			__lowercase=2			,			__lowercase=0.02			,			__lowercase=1E-1_2			,			__lowercase=1			,			__lowercase=0			,			__lowercase=2			,			__lowercase="absolute"			,			__lowercase=False			,			__lowercase="none"			,			**__lowercase			,			)	->     int:
        super().__init__(pad_token_id=__lowercase			,			bos_token_id=__lowercase			,			eos_token_id=__lowercase			,			**__lowercase)

        __UpperCamelCase					:Optional[int]        =		vocab_size
        __UpperCamelCase					:Tuple        =		hidden_size
        __UpperCamelCase					:str        =		num_hidden_layers
        __UpperCamelCase					:List[Any]        =		num_attention_heads
        __UpperCamelCase					:Optional[int]        =		hidden_act
        __UpperCamelCase					:Optional[Any]        =		intermediate_size
        __UpperCamelCase					:List[str]        =		hidden_dropout_prob
        __UpperCamelCase					:List[Any]        =		attention_probs_dropout_prob
        __UpperCamelCase					:Any        =		max_position_embeddings
        __UpperCamelCase					:Dict        =		type_vocab_size
        __UpperCamelCase					:List[Any]        =		initializer_range
        __UpperCamelCase					:Union[str, Any]        =		layer_norm_eps
        __UpperCamelCase					:Optional[Any]        =		position_embedding_type
        __UpperCamelCase					:Optional[int]        =		quant_mode
        __UpperCamelCase					:Tuple        =		force_dequant


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    @property
    def       UpperCamelCase__       (     self)	->     Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            __UpperCamelCase					:Dict        =		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
            __UpperCamelCase					:Tuple        =		{0: '''batch''', 1: '''sequence'''}
        return OrderedDict(
            [
                ('''input_ids''', dynamic_axis),
                ('''attention_mask''', dynamic_axis),
            ])






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					: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=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  (       ):
    '''simple docstring'''







    __UpperCamelCase					:Tuple        =		parse_args()

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

    # Patch sys.argv
    __UpperCamelCase					: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()





