






import os

def   __lowercase       (			):







				"""simple docstring"""






				with open(os.path.dirname(snake_case			) + '''/grid.txt'''			) as f:
								__magic_name__		:int   =   []  # noqa: E741
								for _ in range(2_0			):
												l.append([int(snake_case			) for x in f.readline().split()]			)

								__magic_name__		:Union[str, Any]   =   0

								# right
								for i in range(2_0			):
												for j in range(1_7			):
																__magic_name__		:Any   =   l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
																if temp > maximum:
																				__magic_name__		:Optional[Any]   =   temp

        # down
								for i in range(1_7			):
												for j in range(2_0			):
																__magic_name__		:Optional[Any]   =   l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
																if temp > maximum:
																				__magic_name__		:Optional[int]   =   temp

        # diagonal 1
								for i in range(1_7			):
												for j in range(1_7			):
																__magic_name__		:int   =   l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
																if temp > maximum:
																				__magic_name__		:Optional[Any]   =   temp

        # diagonal 2
								for i in range(1_7			):
												for j in range(3,	2_0			):
																__magic_name__		:int   =   l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
																if temp > maximum:
																				__magic_name__		:List[str]   =   temp
								return maximum


if __name__ == "__main__":
					print(solution())










'''simple docstring'''
from typing import TYPE_CHECKING

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


__snake_case				 =	{
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	['''ConvBertTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ConvBertForMaskedLM''',
		    '''ConvBertForMultipleChoice''',
		    '''ConvBertForQuestionAnswering''',
		    '''ConvBertForSequenceClassification''',
		    '''ConvBertForTokenClassification''',
		    '''ConvBertLayer''',
		    '''ConvBertModel''',
		    '''ConvBertPreTrainedModel''',
		    '''load_tf_weights_in_convbert''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFConvBertForMaskedLM''',
		    '''TFConvBertForMultipleChoice''',
		    '''TFConvBertForQuestionAnswering''',
		    '''TFConvBertForSequenceClassification''',
		    '''TFConvBertForTokenClassification''',
		    '''TFConvBertLayer''',
		    '''TFConvBertModel''',
		    '''TFConvBertPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
		from .tokenization_convbert import ConvBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_convbert_fast import ConvBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_convbert import (
				    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConvBertForMaskedLM,
				    ConvBertForMultipleChoice,
				    ConvBertForQuestionAnswering,
				    ConvBertForSequenceClassification,
				    ConvBertForTokenClassification,
				    ConvBertLayer,
				    ConvBertModel,
				    ConvBertPreTrainedModel,
				    load_tf_weights_in_convbert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_convbert import (
				    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFConvBertForMaskedLM,
				    TFConvBertForMultipleChoice,
				    TFConvBertForQuestionAnswering,
				    TFConvBertForSequenceClassification,
				    TFConvBertForTokenClassification,
				    TFConvBertLayer,
				    TFConvBertModel,
				    TFConvBertPreTrainedModel,
				)


else:
		import sys

		__snake_case				 =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)