




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

import torch_xla.distributed.xla_multiprocessing as xmp





def      UpperCAmelCase  (						)     ->     List[str]:







				'''simple docstring'''


				__lowerCAmelCase					=  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      UpperCAmelCase  (						)     ->     Dict:







				'''simple docstring'''


				__lowerCAmelCase					=  parse_args()

				# Import training_script as a module.
				__lowerCAmelCase					=  Path(args.training_script   )
				sys.path.append(str(script_fpath.parent.resolve()   )   )
				__lowerCAmelCase					=  script_fpath.stem
				__lowerCAmelCase					=  importlib.import_module(UpperCamelCase__   )

				# Patch sys.argv
				__lowerCAmelCase					=  [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()







'''simple docstring'''







import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

_snake_case					: List[str]     			=					[[12, 10], [3, 9]]
_snake_case					: Any     			=					[[3, 4], [7, 4]]
_snake_case					: List[str]     			=					[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
_snake_case					: Any     			=					[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
_snake_case					: Any     			=					[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
_snake_case					: Optional[Any]     			=					[1]
_snake_case					: str     			=					[2]

_snake_case					: Optional[int]     			=					logging.getLogger()
_snake_case					: Optional[Any]     			=					logging.DEBUG
_snake_case					: Tuple     			=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")						,      [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: Tuple	):
						if (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2) or (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2):
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_addition.__name__} returned integer'	)
																		matop.add(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
						elif (np.array(_SCREAMING_SNAKE_CASE	)).shape == (np.array(_SCREAMING_SNAKE_CASE	)).shape:
												logger.info(F'\n\t{test_addition.__name__} with same matrix dims'	)
												_SCREAMING_SNAKE_CASE					=			(np.array(_SCREAMING_SNAKE_CASE	) + np.array(_SCREAMING_SNAKE_CASE	)).tolist()
												_SCREAMING_SNAKE_CASE					=			matop.add(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
												assert theo == act
						else:
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_addition.__name__} with different matrix dims'	)
																		matop.add(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")						,      [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: Optional[Any]						,      _SCREAMING_SNAKE_CASE		: List[str]	):
						if (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2) or (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2):
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_subtraction.__name__} returned integer'	)
																		matop.subtract(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
						elif (np.array(_SCREAMING_SNAKE_CASE	)).shape == (np.array(_SCREAMING_SNAKE_CASE	)).shape:
												logger.info(F'\n\t{test_subtraction.__name__} with same matrix dims'	)
												_SCREAMING_SNAKE_CASE					=			(np.array(_SCREAMING_SNAKE_CASE	) - np.array(_SCREAMING_SNAKE_CASE	)).tolist()
												_SCREAMING_SNAKE_CASE					=			matop.subtract(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
												assert theo == act
						else:
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_subtraction.__name__} with different matrix dims'	)
																		assert matop.subtract(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")						,      [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: List[str]	):
						if (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2) or (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2):
												logger.info(F'\n\t{test_multiplication.__name__} returned integer'	)
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		matop.add(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
						elif (np.array(_SCREAMING_SNAKE_CASE	)).shape == (np.array(_SCREAMING_SNAKE_CASE	)).shape:
												logger.info(F'\n\t{test_multiplication.__name__} meets dim requirements'	)
												_SCREAMING_SNAKE_CASE					=			(np.matmul(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)).tolist()
												_SCREAMING_SNAKE_CASE					=			matop.multiply(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
												assert theo == act
						else:
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(
																		    F'\n\t{test_multiplication.__name__} does not meet dim requirements'	)
																		assert matop.subtract(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)



@pytest.mark.mat_ops
def       _a		(						):
						_SCREAMING_SNAKE_CASE					=			(3.5 * np.array(_SCREAMING_SNAKE_CASE	)).tolist()
						_SCREAMING_SNAKE_CASE					=			matop.scalar_multiply(_SCREAMING_SNAKE_CASE						,      3.5	)
						assert theo == act



@pytest.mark.mat_ops
def       _a		(						):
						_SCREAMING_SNAKE_CASE					=			(np.identity(5	)).tolist()
						_SCREAMING_SNAKE_CASE					=			matop.identity(5	)
						assert theo == act




@pytest.mark.mat_ops
@pytest.mark.parametrize("mat"						,      [mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: Tuple	):
						if (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2):
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_transpose.__name__} returned integer'	)
																		matop.transpose(_SCREAMING_SNAKE_CASE	)
						else:
												_SCREAMING_SNAKE_CASE					=			(np.transpose(_SCREAMING_SNAKE_CASE	)).tolist()
												_SCREAMING_SNAKE_CASE					=			matop.transpose(_SCREAMING_SNAKE_CASE						,      return_map=_SCREAMING_SNAKE_CASE	)
												assert theo == act