




from __future__ import annotations

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

def 					UpperCAmelCase_							(      )					-> tuple[list[int], int]:
      SCREAMING_SNAKE_CASE_							=			[randint(-10_00			,			10_00							) for i in range(10							)]
      SCREAMING_SNAKE_CASE_							=			randint(-50_00			,			50_00							)
      return (arr, r)


lowerCamelCase__ :   str					=							make_dataset()

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[int]			,			__UpperCAmelCase						:	int							)					-> tuple[int, ...]:
      for triplet in permutations(__UpperCAmelCase			,			3							):
            if sum(__UpperCAmelCase							) == target:
                  return tuple(sorted(__UpperCAmelCase							)							)
      return (0, 0, 0)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[int]			,			__UpperCAmelCase						:	int							)					-> tuple[int, int, int]:
      arr.sort()
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)
      for i in range(n - 1							):
            SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			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 					UpperCAmelCase_							(      )					-> tuple[float, float]:
      SCREAMING_SNAKE_CASE_							=			'\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
      SCREAMING_SNAKE_CASE_							=			'\ntriplet_sum1(*dataset)\n'
      SCREAMING_SNAKE_CASE_							=			'\ntriplet_sum2(*dataset)\n'
      SCREAMING_SNAKE_CASE_							=			repeat(setup=__UpperCAmelCase			,			stmt=__UpperCAmelCase			,			repeat=5			,			number=1_00_00							)
      SCREAMING_SNAKE_CASE_							=			repeat(setup=__UpperCAmelCase			,			stmt=__UpperCAmelCase			,			repeat=5			,			number=1_00_00							)
      return (min(__UpperCAmelCase							), min(__UpperCAmelCase							))


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

   testmod()
   lowerCamelCase__ :   Any					=							solution_times()
   print(f'''The time for naive implementation is {times[0]}.''')
   print(f'''The time for optimized implementation is {times[1]}.''')





import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
   import onnxruntime as ort





@nightly
@require_onnxruntime
@require_torch_gpu
class        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 @property
 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )



 @property
 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_							=			ort.SessionOptions()
       SCREAMING_SNAKE_CASE_							=			False
       return options




 def       lowerCAmelCase_  (							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_							=			load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/overture-creations-5sI6fQgYIuo.png'						)
       SCREAMING_SNAKE_CASE_							=			load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'						)
       SCREAMING_SNAKE_CASE_							=			load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'						)

       # using the PNDM scheduler by default
       SCREAMING_SNAKE_CASE_							=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
           'CompVis/stable-diffusion-v1-4'				,				revision='onnx'				,				safety_checker=_lowerCAmelCase				,				feature_extractor=_lowerCAmelCase				,				provider=self.gpu_provider				,				sess_options=self.gpu_options				,				)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			'A red cat sitting on a park bench'

       SCREAMING_SNAKE_CASE_							=			np.random.RandomState(0						)
       SCREAMING_SNAKE_CASE_							=			pipe(
           prompt=_lowerCAmelCase				,				image=_lowerCAmelCase				,				mask_image=_lowerCAmelCase				,				strength=0.75				,				guidance_scale=7.5				,				num_inference_steps=15				,				generator=_lowerCAmelCase				,				output_type='np'				,				)

       SCREAMING_SNAKE_CASE_							=			output.images[0]

       assert image.shape == (512, 512, 3)
       assert np.abs(expected_image - image						).max() < 1E-2