


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()



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()