
"""simple docstring"""




import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCAmelCase__      :    str    							= 5_0_0_0_0
UpperCAmelCase__      :    List[str]    							= 5_0_0_0

UpperCAmelCase__							,				UpperCAmelCase__      :    Optional[int]    							= os.path.split(__file__)
UpperCAmelCase__      :    Optional[Any]    							= os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def lowercase_  (   _snake_case   ,_snake_case						):
							for i in range(_snake_case						):
														SCREAMING_SNAKE_CASE__						:       Dict           =   dataset[i]

@get_duration
def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							for i in range(0   ,len(_snake_case						)   ,_snake_case						):
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   dataset[i : i + batch_size]

@get_duration
def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							with dataset.formatted_as(type=_snake_case						):
														for i in range(_snake_case						):
																					SCREAMING_SNAKE_CASE__						:       Dict           =   dataset[i]

@get_duration
def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case						):
							with dataset.formatted_as(type=_snake_case						):
														for i in range(0   ,_snake_case   ,_snake_case						):
																					SCREAMING_SNAKE_CASE__						:       int           =   dataset[i : i + batch_size]

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   {"""num examples""": SPEED_TEST_N_EXAMPLES}
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [
							    (read, {"""length""": SMALL_TEST}),
							    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
							    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 100}),
							    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_000}),
							    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
							    (read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
							    (read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
							    (read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
							    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
							    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1_000}),
							]

							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   [
							    (read, {"""length""": SMALL_TEST}),
							    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
							    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 100}),
							    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_000}),
							    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
							    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
							    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1_000}),
							]
							with tempfile.TemporaryDirectory() as tmp_dir:
														print("""generating dataset"""						)
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   datasets.Features(
														    {"""list""": datasets.Sequence(datasets.Value("""float32"""						)						), """numbers""": datasets.Value("""float32"""						)}						)
														SCREAMING_SNAKE_CASE__						:       str           =   generate_example_dataset(
														    os.path.join(_snake_case   ,"""dataset.arrow"""						)   ,_snake_case   ,num_examples=_snake_case   ,seq_shapes={"""list""": (100,)}   ,)
														print("""first set of iterations"""						)
														for func, kwargs in functions:
																					print(func.__name__   ,str(_snake_case						)						)
																					SCREAMING_SNAKE_CASE__						:       str           =   func(_snake_case   ,**_snake_case						)

														print("""shuffling dataset"""						)
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   dataset.shuffle()
														print("""Second set of iterations (after shuffling"""						)
														for func, kwargs in functions_shuffled:
																					print("""shuffled """   ,func.__name__   ,str(_snake_case						)						)
																					SCREAMING_SNAKE_CASE__						:       Tuple           =   func(
																					    _snake_case   ,**_snake_case						)

							with open(_snake_case   ,"""wb"""						) as f:
														f.write(json.dumps(_snake_case						).encode("""utf-8"""						)						)


if __name__ == "__main__":  # useful to run the profiler
					benchmark_iterating()






"""simple docstring"""




import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   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 lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       int           =   parse_args()

							# Import training_script as a module.
							SCREAMING_SNAKE_CASE__						:       Dict           =   Path(args.training_script						)
							sys.path.append(str(script_fpath.parent.resolve()						)						)
							SCREAMING_SNAKE_CASE__						:       int           =   script_fpath.stem
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   importlib.import_module(_snake_case						)

							# Patch sys.argv
							SCREAMING_SNAKE_CASE__						:       str           =   [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()




