
'''simple docstring'''



def A (__lowerCamelCase :int						):
						_lowerCAmelCase    		=					(1 + 24 * n) ** 0.5
						return ((1 + root) / 6) % 1 == 0


def A (__lowerCamelCase :int = 5000						):
						_lowerCAmelCase    		=					[(i * (3 * i - 1)) // 2 for i in range(1					,      __lowerCamelCase						)]
						for i, pentagonal_i in enumerate(__lowerCamelCase						):
												for j in range(__lowerCamelCase					,      len(__lowerCamelCase						)						):
																		_lowerCAmelCase    		=					pentagonal_nums[j]
																		_lowerCAmelCase    		=					pentagonal_i + pentagonal_j
																		_lowerCAmelCase    		=					pentagonal_j - pentagonal_i
																		if is_pentagonal(__lowerCamelCase						) and is_pentagonal(__lowerCamelCase						):
																								return b

						return -1


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





'''simple docstring'''





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__    :   List[str]           =					{
    '''configuration_autoformer''': [
        '''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AutoformerConfig''',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   Optional[Any]           =					[
        '''AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''AutoformerForPrediction''',
        '''AutoformerModel''',
        '''AutoformerPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_autoformer import (
        AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        AutoformerConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_autoformer import (
            AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            AutoformerForPrediction,
            AutoformerModel,
            AutoformerPreTrainedModel,
        )

else:
    import sys

    A__    :   Optional[int]           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



