





import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
    import onnxruntime as ort

class    lowerCAmelCase							(					__UpperCamelCase,  unittest.TestCase			):
 # FIXME: add fast tests
 pass


@nightly
@require_onnxruntime
@require_torch_gpu
class    lowerCAmelCase							(					unittest.TestCase			):
 @property
 def      A_	(      self  :      str	)			->	Optional[Any]:
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )
 @property
 def      A_	(      self  :      Optional[Any]	)			->	int:
     lowerCamelCase__    :      Tuple					 =		ort.SessionOptions()
     lowerCamelCase__    :      int					 =		False
     return options
 def      A_	(      self  :      Optional[Any]	)			->	List[str]:
     lowerCamelCase__    :      Optional[int]					 =		load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
     lowerCamelCase__    :      int					 =		load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
     lowerCamelCase__    :      List[str]					 =		OnnxStableDiffusionInpaintPipeline.from_pretrained(
         'runwayml/stable-diffusion-inpainting'			,     revision='onnx'			,     safety_checker=UpperCAmelCase			,     feature_extractor=UpperCAmelCase			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
     pipe.set_progress_bar_config(disable=UpperCAmelCase	)

     lowerCamelCase__    :      List[Any]					 =		'A red cat sitting on a park bench'

     lowerCamelCase__    :      Dict					 =		np.random.RandomState(0	)
     lowerCamelCase__    :      Optional[int]					 =		pipe(
         prompt=UpperCAmelCase			,     image=UpperCAmelCase			,     mask_image=UpperCAmelCase			,     guidance_scale=7.5			,     num_inference_steps=10			,     generator=UpperCAmelCase			,     output_type='np'			,     )
     lowerCamelCase__    :      Any					 =		output.images
     lowerCamelCase__    :      Union[str, Any]					 =		images[0, 255:258, 255:258, -1]

     assert images.shape == (1, 512, 512, 3)
     lowerCamelCase__    :      Union[str, Any]					 =		np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]	)

     assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3







 def      A_	(      self  :      Optional[Any]	)			->	Dict:
     lowerCamelCase__    :      Any					 =		load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
     lowerCamelCase__    :      Optional[Any]					 =		load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
     lowerCamelCase__    :      Tuple					 =		LMSDiscreteScheduler.from_pretrained(
         'runwayml/stable-diffusion-inpainting'			,     subfolder='scheduler'			,     revision='onnx'	)
     lowerCamelCase__    :      str					 =		OnnxStableDiffusionInpaintPipeline.from_pretrained(
         'runwayml/stable-diffusion-inpainting'			,     revision='onnx'			,     scheduler=UpperCAmelCase			,     safety_checker=UpperCAmelCase			,     feature_extractor=UpperCAmelCase			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
     pipe.set_progress_bar_config(disable=UpperCAmelCase	)

     lowerCamelCase__    :      str					 =		'A red cat sitting on a park bench'

     lowerCamelCase__    :      Tuple					 =		np.random.RandomState(0	)
     lowerCamelCase__    :      int					 =		pipe(
         prompt=UpperCAmelCase			,     image=UpperCAmelCase			,     mask_image=UpperCAmelCase			,     guidance_scale=7.5			,     num_inference_steps=20			,     generator=UpperCAmelCase			,     output_type='np'			,     )
     lowerCamelCase__    :      str					 =		output.images
     lowerCamelCase__    :      Tuple					 =		images[0, 255:258, 255:258, -1]

     assert images.shape == (1, 512, 512, 3)
     lowerCamelCase__    :      Dict					 =		np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]	)

     assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3










from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat
def 		UpperCamelCase	(   )	->     tuple[list[int], int]:
   UpperCamelCase    :  int							=	[randint(-1000       ,     1000	) for i in range(10	)]
   UpperCamelCase    :  Dict							=	randint(-5000       ,     5000	)
   return (arr, r)


__UpperCAmelCase          =       make_dataset()
def 		UpperCamelCase	(   snake_case__       :    list[int]       ,     snake_case__       :    int	)	->     tuple[int, ...]:
   for triplet in permutations(snake_case__       ,     3	):
      if sum(snake_case__	) == target:
         return tuple(sorted(snake_case__	)	)
   return (0, 0, 0)
def 		UpperCamelCase	(   snake_case__       :    list[int]       ,     snake_case__       :    int	)	->     tuple[int, int, int]:
   arr.sort()
   UpperCamelCase    :  List[str]							=	len(snake_case__	)
   for i in range(n - 1	):
      UpperCamelCase	,     UpperCamelCase    :  Optional[Any]							=	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]:
   UpperCamelCase    :  Any							=	'\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
   UpperCamelCase    :  Optional[Any]							=	'\ntriplet_sum1(*dataset)\n'
   UpperCamelCase    :  Dict							=	'\ntriplet_sum2(*dataset)\n'
   UpperCamelCase    :  Optional[int]							=	repeat(setup=snake_case__       ,     stmt=snake_case__       ,     repeat=5       ,     number=10000	)
   UpperCamelCase    :  Any							=	repeat(setup=snake_case__       ,     stmt=snake_case__       ,     repeat=5       ,     number=10000	)
   return (min(snake_case__	), min(snake_case__	))


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

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



