
"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__      :    Optional[Any]    							= logging.get_logger(__name__)

UpperCAmelCase__      :    Tuple    							= {
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						int					       =   '''luke'''



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=5_02_67   ,       SCREAMING_SNAKE_CASE__=50_00_00   ,       SCREAMING_SNAKE_CASE__=7_68   ,       SCREAMING_SNAKE_CASE__=2_56   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=30_72   ,       SCREAMING_SNAKE_CASE__="gelu"   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=5_12   ,       SCREAMING_SNAKE_CASE__=2   ,       SCREAMING_SNAKE_CASE__=0.02   ,       SCREAMING_SNAKE_CASE__=1E-12   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=1   ,       SCREAMING_SNAKE_CASE__=0   ,       SCREAMING_SNAKE_CASE__=2   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		Tuple:



											"""simple docstring"""




											super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__   ,       bos_token_id=SCREAMING_SNAKE_CASE__   ,       eos_token_id=SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       List[str]           =   vocab_size
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   entity_vocab_size
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   hidden_size
											SCREAMING_SNAKE_CASE__						:       int           =   entity_emb_size
											SCREAMING_SNAKE_CASE__						:       Tuple           =   num_hidden_layers
											SCREAMING_SNAKE_CASE__						:       Tuple           =   num_attention_heads
											SCREAMING_SNAKE_CASE__						:       int           =   hidden_act
											SCREAMING_SNAKE_CASE__						:       List[str]           =   intermediate_size
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   hidden_dropout_prob
											SCREAMING_SNAKE_CASE__						:       Any           =   attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE__						:       Tuple           =   max_position_embeddings
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   type_vocab_size
											SCREAMING_SNAKE_CASE__						:       int           =   initializer_range
											SCREAMING_SNAKE_CASE__						:       str           =   layer_norm_eps
											SCREAMING_SNAKE_CASE__						:       Any           =   use_entity_aware_attention
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   classifier_dropout






"""simple docstring"""




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

import torch_xla.distributed.xla_multiprocessing as xmp

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

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

							return parser.parse_args()

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       int           =   parse_args()

							# Import training_script as a module.
							SCREAMING_SNAKE_CASE__						:       Dict           =   Path(args.training_script						)
							sys.path.append(str(script_fpath.parent.resolve()						)						)
							SCREAMING_SNAKE_CASE__						:       int           =   script_fpath.stem
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   importlib.import_module(_snake_case						)

							# Patch sys.argv
							SCREAMING_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()




