code: 
  
  
  import json
  import os
  import tempfile
  
  import datasets
  from utils import generate_example_dataset, get_duration
  
  
  A_    :					str             =500000
  
  A_       ,	A_    :					int             =os.path.split(__file__)
  A_    :					List[str]             =os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
  
  
  
  
  @get_duration
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		dataset.select(range(0       ,    len(UpperCAmelCase__      )       ,    2      )      )
  
  
  
  
  @get_duration
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		dataset.sort("""numbers"""      )
  
  
  
  
  @get_duration
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		dataset.shuffle()
  
  
  
  
  @get_duration
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		dataset.train_test_split(0.1      )
  
  
  
  
  @get_duration
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__=10      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						for shard_id in range(UpperCAmelCase__      ):
  												a_					=		dataset.shard(UpperCAmelCase__       ,    UpperCAmelCase__      )
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		{"""num examples""": SPEED_TEST_N_EXAMPLES}
  						a_					=		(select, sort, shuffle, train_test_split, shard)
  						with tempfile.TemporaryDirectory() as tmp_dir:
  												print("""generating dataset"""      )
  												a_					=		datasets.Features({"""text""": datasets.Value("""string"""      ), """numbers""": datasets.Value("""float32"""      )}      )
  												a_					=		generate_example_dataset(
  												    os.path.join(UpperCAmelCase__       ,    """dataset.arrow"""      )       ,    UpperCAmelCase__       ,    num_examples=UpperCAmelCase__      )
  												print("""Functions"""      )
  												for func in functions:
  																		print(func.__name__      )
  																		a_					=		func(UpperCAmelCase__      )
  
  						with open(UpperCAmelCase__       ,    """wb"""      ) as f:
  												f.write(json.dumps(UpperCAmelCase__      ).encode("""utf-8"""      )      )
  
  
  if __name__ == "__main__":  # useful to run the profiler
  	benchmark_indices_mapping()
style_context: 
  
  
  import importlib
  import sys
  from argparse import REMAINDER, ArgumentParser
  from pathlib import Path
  
  import torch_xla.distributed.xla_multiprocessing as xmp
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		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   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		parse_args()
  
  						# Import training_script as a module.
  						a_					=		Path(args.training_script      )
  						sys.path.append(str(script_fpath.parent.resolve()      )      )
  						a_					=		script_fpath.stem
  						a_					=		importlib.import_module(UpperCAmelCase__      )
  
  						# Patch sys.argv
  						a_					=		[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()