



def 		a__			(		_UpperCamelCase			: int = 60_08_51_47_51_43							):

   try:
      __lowerCamelCase													=    int(_UpperCamelCase							)
   except (TypeError, ValueError):
      raise TypeError('''Parameter n must be int or castable to int.'''							)
   if n <= 0:
      raise ValueError('''Parameter n must be greater than or equal to one.'''							)
   __lowerCamelCase													=    2
   __lowerCamelCase													=    0
   if n == 2:
      return 2
   while n > 2:
      while n % i != 0:
         i += 1
      __lowerCamelCase													=    i
      while n % i == 0:
         __lowerCamelCase													=    n // i
      i += 1
   return int(_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""")






