






"""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	(							)						->		Dict:

       """simple docstring"""





       __magic_name__										=						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        A	(							)						->		int:

       """simple docstring"""





       __magic_name__										=						parse_args()

       # Import training_script as a module.
       __magic_name__										=						Path(args.training_script     )
       sys.path.append(str(script_fpath.parent.resolve()     )     )
       __magic_name__										=						script_fpath.stem
       __magic_name__										=						importlib.import_module(__snake_case     )

       # Patch sys.argv
       __magic_name__										=						[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()







"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case   :   int    		=		logging.get_logger(__name__)

snake_case   :   List[str]    		=		{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}



class 					UpperCamelCase__   (							a_):







    """simple docstring"""
    __UpperCAmelCase											=   """nystromformer"""







    def __init__(		self   :  Dict							,						UpperCamelCase_   :  Optional[Any]=3_0_0_0_0							,						UpperCamelCase_   :  Dict=7_6_8							,						UpperCamelCase_   :  Tuple=1_2							,						UpperCamelCase_   :  Any=1_2							,						UpperCamelCase_   :  List[str]=3_0_7_2							,						UpperCamelCase_   :  Tuple="gelu_new"							,						UpperCamelCase_   :  Optional[Any]=0.1							,						UpperCamelCase_   :  str=0.1							,						UpperCamelCase_   :  Tuple=5_1_0							,						UpperCamelCase_   :  Optional[int]=2							,						UpperCamelCase_   :  Optional[int]=6_4							,						UpperCamelCase_   :  Tuple=6_4							,						UpperCamelCase_   :  Optional[int]=6_5							,						UpperCamelCase_   :  Optional[int]=False							,						UpperCamelCase_   :  List[str]=0.02							,						UpperCamelCase_   :  List[Any]=1e-5							,						UpperCamelCase_   :  Union[str, Any]=1							,						UpperCamelCase_   :  Tuple=0							,						UpperCamelCase_   :  List[Any]=2							,						**UpperCamelCase_   :  str							,						):





           '''simple docstring'''

           __magic_name__										=						vocab_size
           __magic_name__										=						max_position_embeddings
           __magic_name__										=						hidden_size
           __magic_name__										=						num_hidden_layers
           __magic_name__										=						num_attention_heads
           __magic_name__										=						intermediate_size
           __magic_name__										=						hidden_act
           __magic_name__										=						hidden_dropout_prob
           __magic_name__										=						attention_probs_dropout_prob
           __magic_name__										=						initializer_range
           __magic_name__										=						type_vocab_size
           __magic_name__										=						segment_means_seq_len
           __magic_name__										=						num_landmarks
           __magic_name__										=						conv_kernel_size
           __magic_name__										=						inv_coeff_init_option
           __magic_name__										=						layer_norm_eps
           super().__init__(pad_token_id=UpperCamelCase_							,						bos_token_id=UpperCamelCase_							,						eos_token_id=UpperCamelCase_							,						**UpperCamelCase_   )