

'''simple docstring'''

import torch

from diffusers import StableDiffusionPipeline


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

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

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





'''simple docstring'''

from collections import defaultdict
from math import gcd

def 					__UpperCamelCase				(						lowercase__    :	int = 1_50_00_00			):






     '''simple docstring'''





     __lowercase      		=defaultdict(lowercase__			)
     __lowercase      		=2
     while 2 * euclid_m * (euclid_m + 1) <= limit:
          for euclid_n in range((euclid_m % 2) + 1,       lowercase__,       2			):
               if gcd(lowercase__,       lowercase__			) > 1:
                    continue
               __lowercase      		=2 * euclid_m * (euclid_m + euclid_n)
               for perimeter in range(lowercase__,       limit + 1,       lowercase__			):
                    frequencies[perimeter] += 1
          euclid_m += 1
     return sum(1 for frequency in frequencies.values() if frequency == 1			)


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


