






def 		a__			(		_UpperCamelCase			: int = 50_00_00_00							):
   __lowerCamelCase													=    set()
   __lowerCamelCase													=    int((limit - 24) ** (1 / 2)							)

   __lowerCamelCase													=    set(range(3 ,prime_square_limit + 1 ,2							)							)
   primes.add(2							)
   for p in range(3 ,prime_square_limit + 1 ,2							):
      if p not in primes:
         continue
      primes.difference_update(set(range(p * p ,prime_square_limit + 1 ,_UpperCamelCase							)							)							)

   for primea in primes:
      __lowerCamelCase													=    primea * primea
      for primea in primes:
         __lowerCamelCase													=    primea * primea * primea
         if square + cube >= limit - 16:
            break
         for primea in primes:
            __lowerCamelCase													=    primea * primea * primea * primea
            __lowerCamelCase													=    square + cube + tetr
            if total >= limit:
               break
            ret.add(_UpperCamelCase							)

   return len(_UpperCamelCase							)


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











import torch

from diffusers import StableDiffusionPipeline


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

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

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






