

from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase							:						Union[str, Any]     			=		logging.get_logger(__name__)

lowerCAmelCase							:						List[str]     			=		{
    """nghuyong/ernie-1.0-base-zh""": """https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json""",
    """nghuyong/ernie-2.0-base-en""": """https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json""",
    """nghuyong/ernie-2.0-large-en""": """https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json""",
    """nghuyong/ernie-3.0-base-zh""": """https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json""",
    """nghuyong/ernie-3.0-medium-zh""": """https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json""",
    """nghuyong/ernie-3.0-mini-zh""": """https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json""",
    """nghuyong/ernie-3.0-micro-zh""": """https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json""",
    """nghuyong/ernie-3.0-nano-zh""": """https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json""",
    """nghuyong/ernie-gram-zh""": """https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json""",
    """nghuyong/ernie-health-zh""": """https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json""",
}



class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							List[Any]    							=  '''ernie'''



			def __init__(				self						:  Optional[int]     ,		lowerCAmelCase__						:  int=3_0522     ,		lowerCAmelCase__						:  int=768     ,		lowerCAmelCase__						:  List[Any]=12     ,		lowerCAmelCase__						:  str=12     ,		lowerCAmelCase__						:  Optional[int]=3072     ,		lowerCAmelCase__						:  str="gelu"     ,		lowerCAmelCase__						:  List[str]=0.1     ,		lowerCAmelCase__						:  str=0.1     ,		lowerCAmelCase__						:  List[str]=512     ,		lowerCAmelCase__						:  List[Any]=2     ,		lowerCAmelCase__						:  Dict=3     ,		lowerCAmelCase__						:  Tuple=False     ,		lowerCAmelCase__						:  str=0.02     ,		lowerCAmelCase__						:  str=1E-12     ,		lowerCAmelCase__						:  Any=0     ,		lowerCAmelCase__						:  Union[str, Any]="absolute"     ,		lowerCAmelCase__						:  Dict=True     ,		lowerCAmelCase__						:  Any=None     ,		**lowerCAmelCase__						:  Optional[int]     ,		):
									super().__init__(pad_token_id=lowerCAmelCase__     ,		**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				List[str]							= vocab_size
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= hidden_size
									SCREAMING_SNAKE_CASE_:				Dict							= num_hidden_layers
									SCREAMING_SNAKE_CASE_:				Optional[int]							= num_attention_heads
									SCREAMING_SNAKE_CASE_:				int							= hidden_act
									SCREAMING_SNAKE_CASE_:				List[Any]							= intermediate_size
									SCREAMING_SNAKE_CASE_:				str							= hidden_dropout_prob
									SCREAMING_SNAKE_CASE_:				List[str]							= attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_:				str							= max_position_embeddings
									SCREAMING_SNAKE_CASE_:				Dict							= type_vocab_size
									SCREAMING_SNAKE_CASE_:				List[str]							= task_type_vocab_size
									SCREAMING_SNAKE_CASE_:				str							= use_task_id
									SCREAMING_SNAKE_CASE_:				int							= initializer_range
									SCREAMING_SNAKE_CASE_:				List[Any]							= layer_norm_eps
									SCREAMING_SNAKE_CASE_:				Tuple							= position_embedding_type
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= use_cache
									SCREAMING_SNAKE_CASE_:				int							= classifier_dropout







class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			@property
			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]):
									if self.task == "multiple-choice":
															SCREAMING_SNAKE_CASE_:				Optional[int]							= {0: "batch", 1: "choice", 2: "sequence"}
									else:
															SCREAMING_SNAKE_CASE_:				List[Any]							= {0: "batch", 1: "sequence"}
									return OrderedDict(
									    [
									        ("input_ids", dynamic_axis),
									        ("attention_mask", dynamic_axis),
									        ("token_type_ids", dynamic_axis),
									        ("task_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_   (					):
						SCREAMING_SNAKE_CASE_:				Tuple							= 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_   (					):
						SCREAMING_SNAKE_CASE_:				List[str]							= parse_args()

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

						# Patch sys.argv
						SCREAMING_SNAKE_CASE_:				int							= [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()






