




'''simple docstring'''





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment




@require_huggingface_suite
class 			_snake_case		(     unittest.TestCase							):




			def        lowerCAmelCase__    ( self							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				inspect.getfile(accelerate.test_utils							)
										snake_case_      =				os.path.sep.join(
										    mod_file.split(os.path.sep							)[:-1] + ["scripts", "external_deps", "test_metrics.py"]							)

										from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

										snake_case_      =				test_metrics




			@require_cpu
			def        lowerCAmelCase__    ( self							)    ->    List[str]:

										'''simple docstring'''




										debug_launcher(self.test_metrics.main    ,							num_processes=1							)




			@require_cpu
			def        lowerCAmelCase__    ( self							)    ->    Dict:

										'''simple docstring'''




										debug_launcher(self.test_metrics.main							)




			@require_single_gpu
			def        lowerCAmelCase__    ( self							)    ->    Union[str, Any]:

										'''simple docstring'''




										self.test_metrics.main()




			@require_multi_gpu
			def        lowerCAmelCase__    ( self							)    ->    List[str]:

										'''simple docstring'''




										print(F'Found {torch.cuda.device_count()} devices.'							)
										snake_case_      =				['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
										with patch_environment(omp_num_threads=1							):
																	execute_subprocess_async(_A    ,							env=os.environ.copy()							)









'''simple docstring'''





from __future__ import annotations

import numpy as np


def 					UpperCamelCase_(     snake_case							:	np.ndarray					):

							'''simple docstring'''






							snake_case_ ,				snake_case_      =				np.shape(snake_case					)
							if rows != columns:
														snake_case_      =				(
														    "'table' has to be of square shaped array but got a "
														    f'{rows}x{columns} array:\n{table}'
														)
														raise ValueError(snake_case					)

							snake_case_      =				np.zeros((rows, columns)					)
							snake_case_      =				np.zeros((rows, columns)					)
							for i in range(snake_case					):
														for j in range(snake_case					):
																					snake_case_      =				sum(lower[i][k] * upper[k][j] for k in range(snake_case					)					)
																					if upper[j][j] == 0:
																												raise ArithmeticError("No LU decomposition exists"					)
																					snake_case_      =				(table[i][j] - total) / upper[j][j]
														snake_case_      =				1
														for j in range(snake_case							,    snake_case					):
																					snake_case_      =				sum(lower[i][k] * upper[k][j] for k in range(snake_case					)					)
																					snake_case_      =				table[i][j] - total
							return lower, upper


if __name__ == "__main__":
		import doctest

		doctest.testmod()




