"""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__    (   _a					,					unittest.TestCase	):
   # FIXME: add fast tests
   pass






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


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



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


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



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


   def 	snake_case__	(     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__				 =							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  )

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

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

        assert images.shape == (1, 512, 512, 3)
        lowercase__				 =							np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]  )

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


   def 	snake_case__	(     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__				 =							LMSDiscreteScheduler.from_pretrained(
            "runwayml/stable-diffusion-inpainting",				subfolder="scheduler",				revision="onnx"  )
        lowercase__				 =							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  )

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

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

        assert images.shape == (1, 512, 512, 3)
        lowercase__				 =							np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]  )

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





"""simple docstring"""


import itertools
import math







def 					__a   (		A      ):






     '''simple docstring'''



     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5    ,				int(math.sqrt(A      ) + 1      )    ,				6      ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True







def 					__a   (		):






     '''simple docstring'''



     lowercase__				 =							2
     while True:
          if is_prime(A      ):
               yield num
          num += 1







def 					__a   (		A = 1_00_01      ):






     '''simple docstring'''



     return next(itertools.islice(prime_generator()    ,				nth - 1    ,				A      )      )


if __name__ == "__main__":
 print(F'{solution() = }')




