






"""simple docstring"""



import sys



def 						lowercase    (				__snake_case				: Dict	):
		lowercase_ : int							       =   len(__snake_case	)
		lowercase_ : Tuple							       =   [[0 for x in range(__snake_case	)] for x in range(__snake_case	)]
		lowercase_ : int							       =   [[0 for x in range(__snake_case	)] for x in range(__snake_case	)]

		for chain_length in range(2		,					__snake_case	):
				for a in range(1		,					n - chain_length + 1	):
						lowercase_ : int							       =   a + chain_length - 1

						lowercase_ : str							       =   sys.maxsize
						for c in range(__snake_case		,					__snake_case	):
								lowercase_ : int							       =   (
								    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
								)
								if cost < matrix[a][b]:
										lowercase_ : Optional[int]							       =   cost
										lowercase_ : str							       =   c
		return matrix, sol



def 						lowercase    (				__snake_case				: Any		,					__snake_case				: List[str]		,					__snake_case				: Optional[int]	):
		if i == j:
				print('''A''' + str(__snake_case	)		,					end=''' '''	)
		else:
				print('''('''		,					end=''' '''	)
				print_optiomal_solution(__snake_case		,					__snake_case		,					optimal_solution[i][j]	)
				print_optiomal_solution(__snake_case		,					optimal_solution[i][j] + 1		,					__snake_case	)
				print(''')'''		,					end=''' '''	)



def 						lowercase    (				):
		lowercase_ : Optional[int]							       =   [3_0, 3_5, 1_5, 5, 1_0, 2_0, 2_5]
		lowercase_ : Optional[int]							       =   len(__snake_case	)
		# Size of matrix created from above array will be
		# 30*35 35*15 15*5 5*10 10*20 20*25
		lowercase_       ,					lowercase_ : Optional[Any]							       =   matrix_chain_order(__snake_case	)

		print('''No. of Operation required: ''' + str(matrix[1][n - 1]	)	)
		print_optiomal_solution(__snake_case		,					1		,					n - 1	)


if __name__ == "__main__":
						main()











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A		:       List[Any]  				=		{
    '''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       List[str]  				=		[
						    '''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MegaForCausalLM''',
						    '''MegaForMaskedLM''',
						    '''MegaForMultipleChoice''',
						    '''MegaForQuestionAnswering''',
						    '''MegaForSequenceClassification''',
						    '''MegaForTokenClassification''',
						    '''MegaModel''',
						    '''MegaPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mega import (
												    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MegaForCausalLM,
												    MegaForMaskedLM,
												    MegaForMultipleChoice,
												    MegaForQuestionAnswering,
												    MegaForSequenceClassification,
												    MegaForTokenClassification,
												    MegaModel,
												    MegaPreTrainedModel,
												)

else:
						import sys

						__A		:       Any  				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



