code: 
  
  
  
  
  
  
  """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()
style_context: 
  
  
  
  
  
  
  """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_   )