
import json
import logging
import os
import subprocess
from argparse import ArgumentParser


A__       :   List[str]   						=					logging.getLogger(__name__)



def      _a     (   ):
			lowerCAmelCase__ :    str				      =			ArgumentParser()
			lowerCAmelCase__ :    List[Any]				      =			parser.parse_known_args()
			for arg in unknown:
						if arg.startswith(('''-''', '''--''')    ):
									parser.add_argument(arg.split('''='''    )[0]    )

			return parser.parse_args()



def      _a     (   ):
			lowerCAmelCase__ :    Optional[Any]				      =			parse_args()
			lowerCAmelCase__ :    Tuple				      =			8888
			lowerCAmelCase__ :    Any				      =			int(os.environ['''SM_NUM_GPUS''']    )
			lowerCAmelCase__ :    Optional[int]				      =			json.loads(os.environ['''SM_HOSTS''']    )
			lowerCAmelCase__ :    Optional[Any]				      =			len(__UpperCamelCase    )
			lowerCAmelCase__ :    str				      =			os.environ["""SM_CURRENT_HOST"""]
			lowerCAmelCase__ :    Tuple				      =			hosts.index(__UpperCamelCase    )
			lowerCAmelCase__ :    Tuple				      =			"""INFO"""

			if num_nodes > 1:
						lowerCAmelCase__ :    List[str]				      =			f'''python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]    )}'''
			else:
						lowerCAmelCase__ :    Dict				      =			f'''python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]    )}'''
			try:
						subprocess.run(__UpperCamelCase	,shell=__UpperCamelCase    )
			except Exception as e:
						logger.info(__UpperCamelCase    )


if __name__ == "__main__":
							main()





import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def      _a     (   __UpperCamelCase       :  NDArray[floataa]	,__UpperCamelCase       :  NDArray[floataa]    ):
			lowerCAmelCase__      ,				lowerCAmelCase__ :    Any				      =			np.shape(__UpperCamelCase    )

			lowerCAmelCase__ :    NDArray[floataa]				      =			np.zeros((rows, 1)	,dtype=__UpperCamelCase    )
			for row in reversed(range(__UpperCamelCase    )    ):
						lowerCAmelCase__ :    List[str]				      =			0
						for col in range(row + 1	,__UpperCamelCase    ):
									total += coefficients[row, col] * x[col]

						lowerCAmelCase__ :    int				      =			(vector[row] - total) / coefficients[row, row]

			return x



def      _a     (   __UpperCamelCase       :  NDArray[floataa]	,__UpperCamelCase       :  NDArray[floataa]    ):
			lowerCAmelCase__      ,				lowerCAmelCase__ :    str				      =			np.shape(__UpperCamelCase    )
			if rows != columns:
						return np.array(()	,dtype=__UpperCamelCase    )

			# augmented matrix
			lowerCAmelCase__ :    NDArray[floataa]				      =			np.concatenate((coefficients, vector)	,axis=1    )
			lowerCAmelCase__ :    List[str]				      =			augmented_mat.astype('''float64'''    )

			# scale the matrix leaving it triangular
			for row in range(rows - 1    ):
						lowerCAmelCase__ :    str				      =			augmented_mat[row, row]
						for col in range(row + 1	,__UpperCamelCase    ):
									lowerCAmelCase__ :    Optional[int]				      =			augmented_mat[col, row] / pivot
									augmented_mat[col, :] -= factor * augmented_mat[row, :]

			lowerCAmelCase__ :    int				      =			retroactive_resolution(
			    augmented_mat[:, 0:columns]	,augmented_mat[:, columns : columns + 1]    )

			return x


if __name__ == "__main__":
							import doctest

							doctest.testmod()



