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 				lowercase       ( a__			,						unittest.TestCase    ):
				# FIXME: add fast tests
				pass







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




				@property
				def 					a							(      self ):
											return (
											    "CUDAExecutionProvider",
											    {
											        "gpu_mem_limit": "15000000000",  # 15GB
											        "arena_extend_strategy": "kSameAsRequested",
											    },
											)




				@property
				def 					a							(      self ):
											snake_case_           =					ort.SessionOptions()
											snake_case_           =					False
											return options




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

											snake_case_           =					'''A red cat sitting on a park bench'''

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

											assert images.shape == (1, 512, 512, 3)
											snake_case_           =					np.array([0.25_14, 0.30_07, 0.35_17, 0.17_90, 0.23_82, 0.31_67, 0.19_44, 0.22_73, 0.24_64] )

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




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

											snake_case_           =					'''A red cat sitting on a park bench'''

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

											assert images.shape == (1, 512, 512, 3)
											snake_case_           =					np.array([0.00_86, 0.00_77, 0.00_83, 0.00_93, 0.01_07, 0.01_39, 0.00_94, 0.00_97, 0.01_25] )

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







def       __lowerCamelCase			(				UpperCamelCase__ = 50000000   ):







							'''simple docstring'''






							snake_case_           =					set()
							snake_case_           =					int((limit - 24) ** (1 / 2)   )

							snake_case_           =					set(range(3     ,							prime_square_limit + 1     ,							2   )   )
							primes.add(2   )
							for p in range(3     ,							prime_square_limit + 1     ,							2   ):
														if p not in primes:
																					continue
														primes.difference_update(set(range(p * p     ,							prime_square_limit + 1     ,							UpperCamelCase__   )   )   )

							for primea in primes:
														snake_case_           =					primea * primea
														for primea in primes:
																					snake_case_           =					primea * primea * primea
																					if square + cube >= limit - 16:
																												break
																					for primea in primes:
																												snake_case_           =					primea * primea * primea * primea
																												snake_case_           =					square + cube + tetr
																												if total >= limit:
																																			break
																												ret.add(UpperCamelCase__   )

							return len(UpperCamelCase__   )


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




