from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__      		=					logging.get_logger(__name__)

lowerCAmelCase__      		=					{
    'google/mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'
}
class 							a_					(		_lowerCAmelCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
 UpperCAmelCase_      					=   """mobilebert"""




 def __init__(     self   :    int     ,       lowercase__   :    str=30_522     ,       lowercase__   :    List[Any]=512     ,       lowercase__   :    Any=24     ,       lowercase__   :    List[str]=4     ,       lowercase__   :    Optional[Any]=512     ,       lowercase__   :    List[str]="relu"     ,       lowercase__   :    Optional[Any]=0.0     ,       lowercase__   :    Tuple=0.1     ,       lowercase__   :    Tuple=512     ,       lowercase__   :    int=2     ,       lowercase__   :    List[Any]=0.02     ,       lowercase__   :    List[Any]=1e-12     ,       lowercase__   :    int=0     ,       lowercase__   :    List[str]=128     ,       lowercase__   :    str=True     ,       lowercase__   :    Any=True     ,       lowercase__   :    Optional[int]=128     ,       lowercase__   :    List[Any]=False     ,       lowercase__   :    Optional[int]=True     ,       lowercase__   :    List[Any]=4     ,       lowercase__   :    List[str]="no_norm"     ,       lowercase__   :    List[str]=True     ,       lowercase__   :    Any=None     ,       **lowercase__   :    Tuple     ,       ):







        '''simple docstring'''



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

        lowerCAmelCase__	      =   vocab_size
        lowerCAmelCase__	      =   hidden_size
        lowerCAmelCase__	      =   num_hidden_layers
        lowerCAmelCase__	      =   num_attention_heads
        lowerCAmelCase__	      =   hidden_act
        lowerCAmelCase__	      =   intermediate_size
        lowerCAmelCase__	      =   hidden_dropout_prob
        lowerCAmelCase__	      =   attention_probs_dropout_prob
        lowerCAmelCase__	      =   max_position_embeddings
        lowerCAmelCase__	      =   type_vocab_size
        lowerCAmelCase__	      =   initializer_range
        lowerCAmelCase__	      =   layer_norm_eps
        lowerCAmelCase__	      =   embedding_size
        lowerCAmelCase__	      =   trigram_input
        lowerCAmelCase__	      =   use_bottleneck
        lowerCAmelCase__	      =   intra_bottleneck_size
        lowerCAmelCase__	      =   use_bottleneck_attention
        lowerCAmelCase__	      =   key_query_shared_bottleneck
        lowerCAmelCase__	      =   num_feedforward_networks
        lowerCAmelCase__	      =   normalization_type
        lowerCAmelCase__	      =   classifier_activation

        if self.use_bottleneck:
               lowerCAmelCase__	      =   intra_bottleneck_size
        else:
               lowerCAmelCase__	      =   hidden_size

        lowerCAmelCase__	      =   classifier_dropout






class 							a_					(		_lowerCAmelCase		):






 '''simple docstring'''






 @property
 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



        if self.task == "multiple-choice":
               lowerCAmelCase__	      =   {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
               lowerCAmelCase__	      =   {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
                ('token_type_ids', 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   A_    (     ):







						'''simple docstring'''


						snake_case_					:str		=     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   A_    (     ):







						'''simple docstring'''


						snake_case_					:Any		=     parse_args()

						# Import training_script as a module.
						snake_case_					:str		=     Path(args.training_script      )
						sys.path.append(str(script_fpath.parent.resolve()      )      )
						snake_case_					:Dict		=     script_fpath.stem
						snake_case_					:int		=     importlib.import_module(_lowercase      )

						# Patch sys.argv
						snake_case_					:Optional[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()
