


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 	snake_case_       (unittest.TestCase				):
					@property
					def 			lowerCamelCase__(				self       :List[str]				) ->   Any:
												return (
												    "CUDAExecutionProvider",
												    {
												        "gpu_mem_limit": "15000000000",  # 15GB
												        "arena_extend_strategy": "kSameAsRequested",
												    },
												)
					@property
					def 			lowerCamelCase__(				self       :str				) ->   Optional[int]:
												a__					=					ort.SessionOptions()
												a__					=					False
												return options



					def 			lowerCamelCase__(				self       :List[str]				) ->   Optional[Any]:
												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__					=					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
												a__					=					OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
												    'CompVis/stable-diffusion-v1-4'      ,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      ,strength=0.75      ,guidance_scale=7.5      ,num_inference_steps=15      ,generator=__snake_case      ,output_type='np'      ,)

												a__					=					output.images[0]

												assert image.shape == (5_12, 5_12, 3)
												assert np.abs(expected_image - image				).max() < 1E-2





from collections import defaultdict
from math import ceil, sqrt






def 							__lowercase       (			__lowerCAmelCase							:    int = 1_0_0_0_0_0_0 ,   __lowerCAmelCase							:    int = 1_0       ):
							a__					=					defaultdict(__lowerCAmelCase       )

							for outer_width in range(3 ,   (t_limit // 4) + 2       ):
														if outer_width * outer_width > t_limit:
																					a__					=					max(
																					    ceil(sqrt(outer_width * outer_width - t_limit       )       ) ,   1       )
														else:
																					a__					=					1

														hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

														for hole_width in range(__lowerCAmelCase ,   outer_width - 1 ,   2       ):
																					count[outer_width * outer_width - hole_width * hole_width] += 1

							return sum(1 for n in count.values() if 1 <= n <= 1_0       )


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




