import random


def 				lowerCAmelCase__       (			a__ )      ->Optional[Any]:




  '''simple docstring'''

  _UpperCamelCase           =    num - 1
  _UpperCamelCase           =    0

  while s % 2 == 0:
    _UpperCamelCase           =    s // 2
    t += 1

  for _ in range(5 ):
    _UpperCamelCase           =    random.randrange(2    ,     num - 1 )
    _UpperCamelCase           =    pow(__a    ,     __a    ,     __a )
    if v != 1:
      _UpperCamelCase           =    0
      while v != (num - 1):
        if i == t - 1:
          return False
        else:
          _UpperCamelCase           =    i + 1
          _UpperCamelCase           =    (v**2) % num
  return True


def 				lowerCAmelCase__       (			a__ )      ->Any:




  '''simple docstring'''

  if num < 2:
    return False

  _UpperCamelCase           =    [
      2,
      3,
      5,
      7,
      11,
      13,
      17,
      19,
      23,
      29,
      31,
      37,
      41,
      43,
      47,
      53,
      59,
      61,
      67,
      71,
      73,
      79,
      83,
      89,
      97,
      101,
      103,
      107,
      109,
      113,
      127,
      131,
      137,
      139,
      149,
      151,
      157,
      163,
      167,
      173,
      179,
      181,
      191,
      193,
      197,
      199,
      211,
      223,
      227,
      229,
      233,
      239,
      241,
      251,
      257,
      263,
      269,
      271,
      277,
      281,
      283,
      293,
      307,
      311,
      313,
      317,
      331,
      337,
      347,
      349,
      353,
      359,
      367,
      373,
      379,
      383,
      389,
      397,
      401,
      409,
      419,
      421,
      431,
      433,
      439,
      443,
      449,
      457,
      461,
      463,
      467,
      479,
      487,
      491,
      499,
      503,
      509,
      521,
      523,
      541,
      547,
      557,
      563,
      569,
      571,
      577,
      587,
      593,
      599,
      601,
      607,
      613,
      617,
      619,
      631,
      641,
      643,
      647,
      653,
      659,
      661,
      673,
      677,
      683,
      691,
      701,
      709,
      719,
      727,
      733,
      739,
      743,
      751,
      757,
      761,
      769,
      773,
      787,
      797,
      809,
      811,
      821,
      823,
      827,
      829,
      839,
      853,
      857,
      859,
      863,
      877,
      881,
      883,
      887,
      907,
      911,
      919,
      929,
      937,
      941,
      947,
      953,
      967,
      971,
      977,
      983,
      991,
      997,
  ]

  if num in low_primes:
    return True

  for prime in low_primes:
    if (num % prime) == 0:
      return False

  return rabin_miller(__a )


def 				lowerCAmelCase__       (			a__ = 1_024 )      ->str:




  '''simple docstring'''

  while True:
    _UpperCamelCase           =    random.randrange(2 ** (keysize - 1)    ,     2 ** (keysize) )
    if is_prime_low_num(__a ):
      return num


if __name__ == "__main__":
   lowerCamelCase__  =					generate_large_prime()
   print(('''Prime number:''', num))
   print(('''is_prime_low_num:''', is_prime_low_num(num)))






"""simple docstring"""





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					(       _lowercase  ,					unittest.TestCase   ):




			"""simple docstring"""







			pass





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




			"""simple docstring"""







			@property
			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Dict:







								"""simple docstring"""
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)




			@property
			def   lowerCamelCase__  (					self    :       List[str]						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    ort.SessionOptions()
								A_								=    False
								return options




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
								A_								=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, revision="onnx"							, safety_checker=_snake_case							, feature_extractor=_snake_case							, provider=self.gpu_provider							, sess_options=self.gpu_options							, )
								pipe.set_progress_bar_config(disable=_snake_case						)

								A_								=    "A red cat sitting on a park bench"

								A_								=    np.random.RandomState(0						)
								A_								=    pipe(
								    prompt=_snake_case							, image=_snake_case							, mask_image=_snake_case							, guidance_scale=7.5							, num_inference_steps=10							, generator=_snake_case							, output_type="np"							, )
								A_								=    output.images
								A_								=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								A_								=    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   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Tuple:







								"""simple docstring"""
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
								A_								=    LMSDiscreteScheduler.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, subfolder="scheduler"							, revision="onnx"						)
								A_								=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, revision="onnx"							, scheduler=_snake_case							, safety_checker=_snake_case							, feature_extractor=_snake_case							, provider=self.gpu_provider							, sess_options=self.gpu_options							, )
								pipe.set_progress_bar_config(disable=_snake_case						)

								A_								=    "A red cat sitting on a park bench"

								A_								=    np.random.RandomState(0						)
								A_								=    pipe(
								    prompt=_snake_case							, image=_snake_case							, mask_image=_snake_case							, guidance_scale=7.5							, num_inference_steps=20							, generator=_snake_case							, output_type="np"							, )
								A_								=    output.images
								A_								=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								A_								=    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




