"""simple docstring"""






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 				_A		( unittest.TestCase			):

      @property
      def        A__ (		self		):







            """simple docstring"""



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

      @property
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			ort.SessionOptions()
            lowercase														=			False
            return options







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
            lowercase														=			load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
            lowercase														=			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
            lowercase														=			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		)

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

            lowercase														=			np.random.RandomState(0		)
            lowercase														=			pipe(
                prompt=__lowerCAmelCase	, image=__lowerCAmelCase	, mask_image=__lowerCAmelCase	, strength=0.7_5	, guidance_scale=7.5	, num_inference_steps=15	, generator=__lowerCAmelCase	, output_type="""np"""	, )

            lowercase														=			output.images[0]

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




"""simple docstring"""






from __future__ import annotations

import math


def     UpperCAmelCase__ (       lowerCAmelCase__  :int	)				->					list[int]:
      '''simple docstring'''


      if num <= 0:
            lowercase														=			f'{num}: Invalid input, please enter a positive integer.'
            raise ValueError(lowerCAmelCase__	)

      lowercase														=			[True] * (num + 1)
      lowercase														=			[]
      lowercase														=			2
      lowercase														=			int(math.sqrt(lowerCAmelCase__	)	)

      while start <= end:
            # If start is a prime
            if sieve[start] is True:
                  prime.append(lowerCAmelCase__	)

                  # Set multiples of start be False
                  for i in range(start * start      ,	num + 1      ,	lowerCAmelCase__	):
                        if sieve[i] is True:
                              lowercase														=			False

            start += 1

      for j in range(end + 1      ,	num + 1	):
            if sieve[j] is True:
                  prime.append(lowerCAmelCase__	)

      return prime


if __name__ == "__main__":
       print(prime_sieve(int(input("""Enter a positive integer: """).strip())))



