
'''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            = {
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}

class     a (					__magic_name__     ):
				_snake_case		     =			MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
				_snake_case		     =			'''mobilebert'''




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					int=3_05_22,     SCREAMING_SNAKE_CASE_						:					List[Any]=5_12,     SCREAMING_SNAKE_CASE_						:					str=24,     SCREAMING_SNAKE_CASE_						:					List[Any]=4,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=5_12,     SCREAMING_SNAKE_CASE_						:					int="relu",     SCREAMING_SNAKE_CASE_						:					Tuple=0.0,     SCREAMING_SNAKE_CASE_						:					Dict=0.1,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=5_12,     SCREAMING_SNAKE_CASE_						:					int=2,     SCREAMING_SNAKE_CASE_						:					Dict=0.02,     SCREAMING_SNAKE_CASE_						:					List[str]=1e-12,     SCREAMING_SNAKE_CASE_						:					List[Any]=0,     SCREAMING_SNAKE_CASE_						:					Any=1_28,     SCREAMING_SNAKE_CASE_						:					Tuple=True,     SCREAMING_SNAKE_CASE_						:					Dict=True,     SCREAMING_SNAKE_CASE_						:					List[str]=1_28,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=False,     SCREAMING_SNAKE_CASE_						:					List[Any]=True,     SCREAMING_SNAKE_CASE_						:					Any=4,     SCREAMING_SNAKE_CASE_						:					Dict="no_norm",     SCREAMING_SNAKE_CASE_						:					Any=True,     SCREAMING_SNAKE_CASE_						:					Optional[int]=None,     **SCREAMING_SNAKE_CASE_						:					str,     ):
										super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )

										snake_case							:  str			=     vocab_size
										snake_case							:  Optional[Any]			=     hidden_size
										snake_case							:  Union[str, Any]			=     num_hidden_layers
										snake_case							:  str			=     num_attention_heads
										snake_case							:  Tuple			=     hidden_act
										snake_case							:  Optional[int]			=     intermediate_size
										snake_case							:  str			=     hidden_dropout_prob
										snake_case							:  Any			=     attention_probs_dropout_prob
										snake_case							:  Dict			=     max_position_embeddings
										snake_case							:  List[str]			=     type_vocab_size
										snake_case							:  int			=     initializer_range
										snake_case							:  List[Any]			=     layer_norm_eps
										snake_case							:  Any			=     embedding_size
										snake_case							:  Tuple			=     trigram_input
										snake_case							:  str			=     use_bottleneck
										snake_case							:  Union[str, Any]			=     intra_bottleneck_size
										snake_case							:  Any			=     use_bottleneck_attention
										snake_case							:  Tuple			=     key_query_shared_bottleneck
										snake_case							:  int			=     num_feedforward_networks
										snake_case							:  Union[str, Any]			=     normalization_type
										snake_case							:  Union[str, Any]			=     classifier_activation

										if self.use_bottleneck:
																snake_case							:  Optional[int]			=     intra_bottleneck_size
										else:
																snake_case							:  Optional[int]			=     hidden_size

										snake_case							:  Optional[Any]			=     classifier_dropout




class     a (					__magic_name__     ):




				@property
				def 						__snake_case				(				self						:					List[str]   ):
										if self.task == "multiple-choice":
																snake_case							:  Any			=     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
																snake_case							:  List[str]			=     {0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										        ('''token_type_ids''', dynamic_axis),
										    ]   )








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

import torch_xla.distributed.xla_multiprocessing as xmp







def  A					(				):
					_snake_case							:       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=UpperCAmelCase	,	default=1	,	help="Number of TPU cores to use (1 or 8)."					)

					# positional
					parser.add_argument(
					    "training_script"	,	type=UpperCAmelCase	,	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=UpperCAmelCase					)

					return parser.parse_args()







def  A					(				):
					_snake_case							:       Any												= parse_args()

					# Import training_script as a module.
					_snake_case							:       Any												= Path(args.training_script					)
					sys.path.append(str(script_fpath.parent.resolve()					)					)
					_snake_case							:       Tuple												= script_fpath.stem
					_snake_case							:       str												= importlib.import_module(UpperCAmelCase					)

					# Patch sys.argv
					_snake_case							:       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()