


from math import sqrt




def        snake_case_					(  snake_case = 1_00_00_00					)  ->    int:
       lowercase__:       int        =       0
       lowercase__:       int        =       0
       lowercase__:       int

       while num_cuboids <= limit:
              max_cuboid_size += 1
              for sum_shortest_sides in range(2      ,							2 * max_cuboid_size + 1					):
                     if sqrt(sum_shortest_sides**2 + max_cuboid_size**2					).is_integer():
                            num_cuboids += (
                                min(snake_case      ,							sum_shortest_sides // 2					)
                                - max(1      ,							sum_shortest_sides - max_cuboid_size					)
                                + 1
                            )

       return max_cuboid_size


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






import torch

from diffusers import StableDiffusionPipeline


__lowerCAmelCase			=							'''path-to-your-trained-model'''
__lowerCAmelCase			=							StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

__lowerCAmelCase			=							'''A photo of sks dog in a bucket'''
__lowerCAmelCase			=							pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')


