




import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD





def 		__A		(	__lowerCamelCase    )      ->       List[Any]:
	def wrapper(*__lowerCamelCase							,			**__lowerCamelCase    ):
		a										=							timeit.default_timer()
		a										=							func(*__lowerCamelCase							,			**__lowerCamelCase    )
		a										=							timeit.default_timer() - starttime
		return delta

	a										=							func.__name__

	return wrapper





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase=100							,			__lowerCamelCase=None    )      ->       Any:
	a										=							[]
	a										=							seq_shapes or {}
	for i in range(__lowerCamelCase    ):
		a										=							{}
		for col_id, (k, v) in enumerate(features.items()    ):
			if isinstance(__lowerCamelCase							,			_ArrayXD    ):
				a										=							np.random.rand(*v.shape    ).astype(v.dtype    )
			elif isinstance(__lowerCamelCase							,			datasets.Value    ):
				if v.dtype == "string":
					a										=							"""The small grey turtle was surprisingly fast when challenged."""
				else:
					a										=							np.random.randint(10							,			size=1    ).astype(v.dtype    ).item()
			elif isinstance(__lowerCamelCase							,			datasets.Sequence    ):
				while isinstance(__lowerCamelCase							,			datasets.Sequence    ):
					a										=							v.feature
				a										=							seq_shapes[k]
				a										=							np.random.rand(*__lowerCamelCase    ).astype(v.dtype    )
			a										=							data

		dummy_data.append((i, example)    )

	return dummy_data





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=100							,			__lowerCamelCase=None    )      ->       Dict:
	a										=							generate_examples(__lowerCamelCase							,			num_examples=__lowerCamelCase							,			seq_shapes=__lowerCamelCase    )

	with ArrowWriter(features=__lowerCamelCase							,			path=__lowerCamelCase    ) as writer:
		for key, record in dummy_data:
			a										=							features.encode_example(__lowerCamelCase    )
			writer.write(__lowerCamelCase    )

		a				, a										=							writer.finalize()

	if not num_final_examples == num_examples:
		raise ValueError(
		    f'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'    )

	a										=							datasets.Dataset.from_file(filename=__lowerCamelCase							,			info=datasets.DatasetInfo(features=__lowerCamelCase    )    )

	return dataset







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

import torch_xla.distributed.xla_multiprocessing as xmp





def 		__A		(	)      ->       Any:
	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=__lowerCamelCase							,			default=1							,			help="""Number of TPU cores to use (1 or 8)."""    )

	# positional
	parser.add_argument(
	    """training_script"""							,			type=__lowerCamelCase							,			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=__lowerCamelCase    )

	return parser.parse_args()





def 		__A		(	)      ->       Union[str, Any]:
	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(__lowerCamelCase    )

	# 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()

