





import os





def 				__lowerCAmelCase(       )							-> Union[str, Any]:




    """simple docstring"""




    with open(os.path.dirname(_SCREAMING_SNAKE_CASE				) + '/grid.txt'				) as f:
        _A     =							[]  # noqa: E741
        for _ in range(20				):
            l.append([int(_SCREAMING_SNAKE_CASE				) for x in f.readline().split()]				)

        _A     =							0

        # right
        for i in range(20				):
            for j in range(17				):
                _A     =							l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
                if temp > maximum:
                    _A     =							temp

        # down
        for i in range(17				):
            for j in range(20				):
                _A     =							l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
                if temp > maximum:
                    _A     =							temp

        # diagonal 1
        for i in range(17				):
            for j in range(17				):
                _A     =							l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
                if temp > maximum:
                    _A     =							temp

        # diagonal 2
        for i in range(17				):
            for j in range(3							,     20				):
                _A     =							l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
                if temp > maximum:
                    _A     =							temp
        return maximum


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












from ... import PretrainedConfig


__A							:			Optional[Any]         =			{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
     __magic_name__   =			'nezha'




     def __init__(    self				,     snake_case_=2_1128				,     snake_case_=768				,     snake_case_=12				,     snake_case_=12				,     snake_case_=3072				,     snake_case_="gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=512				,     snake_case_=64				,     snake_case_=2				,     snake_case_=0.02				,     snake_case_=1E-12				,     snake_case_=0.1				,     snake_case_=0				,     snake_case_=2				,     snake_case_=3				,     snake_case_=True				,     **snake_case_				,     ):
         super().__init__(pad_token_id=snake_case_				,     bos_token_id=snake_case_				,     eos_token_id=snake_case_				,     **snake_case_							)

         _A     =							vocab_size
         _A     =							hidden_size
         _A     =							num_hidden_layers
         _A     =							num_attention_heads
         _A     =							hidden_act
         _A     =							intermediate_size
         _A     =							hidden_dropout_prob
         _A     =							attention_probs_dropout_prob
         _A     =							max_position_embeddings
         _A     =							max_relative_position
         _A     =							type_vocab_size
         _A     =							initializer_range
         _A     =							layer_norm_eps
         _A     =							classifier_dropout
         _A     =							use_cache





