

from typing import List, Union

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


lowerCAmelCase							:						Optional[int]     			=		logging.get_logger(__name__)

lowerCAmelCase							:						Optional[Any]     			=		{
    """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/config.json""",
    # See all LED models at https://huggingface.co/models?filter=led
}



class 						__lowercase       (				a_			):




			"""simple docstring"""







			_UpperCAmelCase     :							List[str]    							=  'led'
			_UpperCAmelCase     :							Union[str, Any]    							=  {
			    'num_attention_heads': 'encoder_attention_heads',
			    'hidden_size': 'd_model',
			    'attention_probs_dropout_prob': 'attention_dropout',
			    'initializer_range': 'init_std',
			}



			def __init__(				self						:  Any     ,		lowerCAmelCase__						:  Dict=5_0265     ,		lowerCAmelCase__						:  Any=1_6384     ,		lowerCAmelCase__						:  Optional[Any]=1024     ,		lowerCAmelCase__						:  List[str]=12     ,		lowerCAmelCase__						:  Optional[Any]=4096     ,		lowerCAmelCase__						:  str=16     ,		lowerCAmelCase__						:  Any=12     ,		lowerCAmelCase__						:  Union[str, Any]=4096     ,		lowerCAmelCase__						:  Tuple=16     ,		lowerCAmelCase__						:  int=0.0     ,		lowerCAmelCase__						:  Union[str, Any]=0.0     ,		lowerCAmelCase__						:  str=True     ,		lowerCAmelCase__						:  Optional[Any]=True     ,		lowerCAmelCase__						:  List[str]="gelu"     ,		lowerCAmelCase__						:  Tuple=1024     ,		lowerCAmelCase__						:  str=0.1     ,		lowerCAmelCase__						:  List[Any]=0.0     ,		lowerCAmelCase__						:  Optional[Any]=0.0     ,		lowerCAmelCase__						:  str=0.02     ,		lowerCAmelCase__						:  Union[str, Any]=2     ,		lowerCAmelCase__						:  Dict=0.0     ,		lowerCAmelCase__						:  Union[str, Any]=1     ,		lowerCAmelCase__						:  Dict=0     ,		lowerCAmelCase__						:  List[Any]=2     ,		lowerCAmelCase__						:  Dict = 512     ,		**lowerCAmelCase__						:  Union[str, Any]     ,		):
									SCREAMING_SNAKE_CASE_:				Dict							= vocab_size
									SCREAMING_SNAKE_CASE_:				List[str]							= max_encoder_position_embeddings
									SCREAMING_SNAKE_CASE_:				Dict							= max_decoder_position_embeddings
									SCREAMING_SNAKE_CASE_:				Any							= d_model
									SCREAMING_SNAKE_CASE_:				Dict							= encoder_ffn_dim
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= encoder_layers
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= encoder_attention_heads
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= decoder_ffn_dim
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= decoder_layers
									SCREAMING_SNAKE_CASE_:				int							= decoder_attention_heads
									SCREAMING_SNAKE_CASE_:				Any							= dropout
									SCREAMING_SNAKE_CASE_:				int							= attention_dropout
									SCREAMING_SNAKE_CASE_:				Optional[int]							= activation_dropout
									SCREAMING_SNAKE_CASE_:				int							= activation_function
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= init_std
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= encoder_layerdrop
									SCREAMING_SNAKE_CASE_:				Tuple							= decoder_layerdrop
									SCREAMING_SNAKE_CASE_:				str							= classifier_dropout
									SCREAMING_SNAKE_CASE_:				Any							= use_cache
									SCREAMING_SNAKE_CASE_:				Any							= encoder_layers
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= attention_window

									super().__init__(
									    pad_token_id=_snake_case     ,		bos_token_id=_snake_case     ,		eos_token_id=_snake_case     ,		is_encoder_decoder=_snake_case     ,		decoder_start_token_id=_snake_case     ,		**_snake_case     ,		)









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()






