

"""simple docstring"""





from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat




def   _A  (  )	->						tuple[list[int], int]:
   '''simple docstring'''


   __lowercase								=						[randint(-1000,			1000) for i in range(10)]
   __lowercase								=						randint(-5000,			5000)
   return (arr, r)


_a       =							make_dataset()




def   _A  (  UpperCamelCase_     :       list[int],			UpperCamelCase_     :       int)	->						tuple[int, ...]:
   '''simple docstring'''


   for triplet in permutations(UpperCamelCase_,			3):
      if sum(UpperCamelCase_) == target:
         return tuple(sorted(UpperCamelCase_))
   return (0, 0, 0)




def   _A  (  UpperCamelCase_     :       list[int],			UpperCamelCase_     :       int)	->						tuple[int, int, int]:
   '''simple docstring'''


   arr.sort()
   __lowercase								=						len(UpperCamelCase_)
   for i in range(n - 1):
      __lowercase		,__lowercase								=						i + 1, n - 1
      while left < right:
         if arr[i] + arr[left] + arr[right] == target:
            return (arr[i], arr[left], arr[right])
         elif arr[i] + arr[left] + arr[right] < target:
            left += 1
         elif arr[i] + arr[left] + arr[right] > target:
            right -= 1
   return (0, 0, 0)




def   _A  (  )	->						tuple[float, float]:
   '''simple docstring'''


   __lowercase								=						"\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n"
   __lowercase								=						"\ntriplet_sum1(*dataset)\n"
   __lowercase								=						"\ntriplet_sum2(*dataset)\n"
   __lowercase								=						repeat(setup=UpperCamelCase_,			stmt=UpperCamelCase_,			repeat=5,			number=10000)
   __lowercase								=						repeat(setup=UpperCamelCase_,			stmt=UpperCamelCase_,			repeat=5,			number=10000)
   return (min(UpperCamelCase_), min(UpperCamelCase_))


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   _a       =							solution_times()
   print(F"The time for naive implementation is {times[0]}.")
   print(F"The time for optimized implementation is {times[1]}.")









'''simple docstring'''




import torch

from diffusers import StableDiffusionPipeline


lowerCamelCase							:Tuple				    =					'''path-to-your-trained-model'''
lowerCamelCase							:Optional[int]				    =					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

lowerCamelCase							:Optional[int]				    =					'''A photo of sks dog in a bucket'''
lowerCamelCase							:List[Any]				    =					pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

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