





'''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 					a_     (				snake_case_      ,    unittest.TestCase       ):

    '''simple docstring'''
    # FIXME: add fast tests
    pass




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

    '''simple docstring'''


    @property
    def 			snake_case_(		self       )		->   Optional[int]:
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )


    @property
    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			ort.SessionOptions()
        _SCREAMING_SNAKE_CASE									=			False
        return options


    def 			snake_case_(		self       )		->   List[Any]:
        _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									=			OnnxStableDiffusionInpaintPipeline.from_pretrained(
            """runwayml/stable-diffusion-inpainting""" ,		revision="""onnx""" ,		safety_checker=A ,		feature_extractor=A ,		provider=self.gpu_provider ,		sess_options=self.gpu_options ,		)
        pipe.set_progress_bar_config(disable=A       )

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

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

        assert images.shape == (1, 512, 512, 3)
        _SCREAMING_SNAKE_CASE									=			np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]       )

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





    def 			snake_case_(		self       )		->   List[str]:
        _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									=			LMSDiscreteScheduler.from_pretrained(
            """runwayml/stable-diffusion-inpainting""" ,		subfolder="""scheduler""" ,		revision="""onnx"""       )
        _SCREAMING_SNAKE_CASE									=			OnnxStableDiffusionInpaintPipeline.from_pretrained(
            """runwayml/stable-diffusion-inpainting""" ,		revision="""onnx""" ,		scheduler=A ,		safety_checker=A ,		feature_extractor=A ,		provider=self.gpu_provider ,		sess_options=self.gpu_options ,		)
        pipe.set_progress_bar_config(disable=A       )

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

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

        assert images.shape == (1, 512, 512, 3)
        _SCREAMING_SNAKE_CASE									=			np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]       )

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










'''simple docstring'''

from __future__ import annotations

import math





def lowerCamelCase						(     __lowerCamelCase :    int    )						->list[int]:

    if num <= 0:
        _SCREAMING_SNAKE_CASE									=			F'{num}: Invalid input, please enter a positive integer.'
        raise ValueError(__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			[True] * (num + 1)
    _SCREAMING_SNAKE_CASE									=			[]
    _SCREAMING_SNAKE_CASE									=			2
    _SCREAMING_SNAKE_CASE									=			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:
                    _SCREAMING_SNAKE_CASE									=			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())))



