def SCREAMING_SNAKE_CASE					(  lowercase_:						int					):
       return n * (2 * n - 1)


def SCREAMING_SNAKE_CASE					(  lowercase_:						int					):
       _UpperCamelCase  :							str	    =	(1 + 24 * n) ** 0.5
       return ((1 + root) / 6) % 1 == 0


def SCREAMING_SNAKE_CASE					(  lowercase_:						int = 144					):
       _UpperCamelCase  :							Optional[Any]	    =	start
       _UpperCamelCase  :							List[str]	    =	hexagonal_num(lowercase_					)
       while not is_pentagonal(lowercase_					):
              n += 1
              _UpperCamelCase  :							Optional[int]	    =	hexagonal_num(lowercase_					)
       return num


if __name__ == "__main__":
 print(f'''{solution()} = ''')









"""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_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment

class    __lowercase							(      unittest.TestCase						):


				'''simple docstring'''


				def    _lowerCamelCase       ( self :  Any   ):
										snake_case__							:       Dict				      =       inspect.getfile(accelerate.test_utils   )
										snake_case__							:       Tuple				      =       os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_sync.py''']   )

				@require_cpu
				def    _lowerCamelCase       ( self :  Optional[Any]   ):
										debug_launcher(test_sync.main   ,num_processes=1   )

				@require_cpu
				def    _lowerCamelCase       ( self :  List[str]   ):
										debug_launcher(test_sync.main   )

				@require_single_gpu
				def    _lowerCamelCase       ( self :  Union[str, Any]   ):
										test_sync.main()





				@require_multi_gpu
				def    _lowerCamelCase       ( self :  List[str]   ):
										print(F'''Found {torch.cuda.device_count()} devices.'''   )
										snake_case__							:       Dict				      =       ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
										with patch_environment(omp_num_threads=1   ):
																execute_subprocess_async(SCREAMING_SNAKE_CASE_   ,env=os.environ.copy()   )


