



'''simple docstring'''




def 		_lowerCAmelCase				(_lowercase			):

       """simple docstring"""

       a__								= (1 + 24 * n) ** 0.5
       return ((1 + root) / 6) % 1 == 0






def 		_lowerCAmelCase				(_lowercase = 50_00			):

       """simple docstring"""

       a__								= [(i * (3 * i - 1)) // 2 for i in range(1   ,					lowercase__			)]
       for i, pentagonal_i in enumerate(lowercase__			):
              for j in range(lowercase__   ,					len(lowercase__			)			):
                     a__								= pentagonal_nums[j]
                     a__								= pentagonal_i + pentagonal_j
                     a__								= pentagonal_j - pentagonal_i
                     if is_pentagonal(lowercase__			) and is_pentagonal(lowercase__			):
                            return b

       return -1


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






"""simple docstring"""






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
			if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import (
			    VersatileDiffusionDualGuidedPipeline,
			    VersatileDiffusionImageVariationPipeline,
			    VersatileDiffusionPipeline,
			    VersatileDiffusionTextToImagePipeline,
			)
else:
			from .modeling_text_unet import UNetFlatConditionModel
			from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
			from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
			from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
			from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline