



'''simple docstring'''



from __future__ import annotations

from collections.abc import Callable

lowerCAmelCase_  :							List[str]             =    list[list[float | int]]
def        UpperCAmelCase (    A	:					List[Any]       ,		A	:					List[str]					):
							SCREAMING_SNAKE_CASE       :   int         =      len(_a					)
							SCREAMING_SNAKE_CASE       :   Matrix         =      [[0 for _ in range(size + 1					)] for _ in range(_a					)]
							SCREAMING_SNAKE_CASE       :   int
							SCREAMING_SNAKE_CASE       :   int
							SCREAMING_SNAKE_CASE       :   int
							SCREAMING_SNAKE_CASE       :   int
							SCREAMING_SNAKE_CASE       :   int
							SCREAMING_SNAKE_CASE       :   float

							for row in range(_a					):
														for col in range(_a					):
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      matrix[row][col]

														SCREAMING_SNAKE_CASE       :   int         =      vector[row][0]

							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      0
							SCREAMING_SNAKE_CASE       :   List[str]         =      0
							while row < size and col < size:
														# pivoting
														SCREAMING_SNAKE_CASE       :   List[str]         =      max((abs(augmented[rowa][col]					), rowa) for rowa in range(_a       ,		_a					)					)[
														    1
														]
														if augmented[pivot_row][col] == 0:
																					col += 1
																					continue
														else:
																					SCREAMING_SNAKE_CASE       :   int         =      augmented[pivot_row], augmented[row]

														for rowa in range(row + 1       ,		_a					):
																					SCREAMING_SNAKE_CASE       :   Dict         =      augmented[rowa][col] / augmented[row][col]
																					SCREAMING_SNAKE_CASE       :   int         =      0
																					for cola in range(col + 1       ,		size + 1					):
																												augmented[rowa][cola] -= augmented[row][cola] * ratio

														row += 1
														col += 1

							# back substitution
							for col in range(1       ,		_a					):
														for row in range(_a					):
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      augmented[row][col] / augmented[col][col]
																					for cola in range(_a       ,		size + 1					):
																												augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
							return [
							    [round(augmented[row][size] / augmented[row][row]       ,		10					)] for row in range(_a					)
							]
def        UpperCAmelCase (    A	:					int					):
							SCREAMING_SNAKE_CASE       :   int         =      len(_a					)
							SCREAMING_SNAKE_CASE       :   Matrix         =      [[0 for _ in range(_a					)] for _ in range(_a					)]
							SCREAMING_SNAKE_CASE       :   Matrix         =      [[0] for _ in range(_a					)]
							SCREAMING_SNAKE_CASE       :   Matrix
							SCREAMING_SNAKE_CASE       :   int
							SCREAMING_SNAKE_CASE       :   int
							SCREAMING_SNAKE_CASE       :   int

							for x_val, y_val in enumerate(_a					):
														for col in range(_a					):
																					SCREAMING_SNAKE_CASE       :   Any         =      (x_val + 1) ** (size - col - 1)
														SCREAMING_SNAKE_CASE       :   Tuple         =      y_val

							SCREAMING_SNAKE_CASE       :   Any         =      solve(_a       ,		_a					)

							def interpolated_func(A	:					Tuple					) -> int:
														return sum(
														    round(coeffs[x_val][0]					) * (var ** (size - x_val - 1))
														    for x_val in range(_a					)					)

							return interpolated_func
def        UpperCAmelCase (    A	:					List[str]					):
							return (
							    1
							    - variable
							    + variable**2
							    - variable**3
							    + variable**4
							    - variable**5
							    + variable**6
							    - variable**7
							    + variable**8
							    - variable**9
							    + variable**10
							)
def        UpperCAmelCase (    A	:					str = question_function       ,		A	:					Optional[int] = 10					):
							SCREAMING_SNAKE_CASE       :   list[int]         =      [func(_a					) for x_val in range(1       ,		order + 1					)]

							SCREAMING_SNAKE_CASE       :   list[Callable[[int], int]]         =      [
							    interpolate(data_points[:max_coeff]					) for max_coeff in range(1       ,		order + 1					)
							]

							SCREAMING_SNAKE_CASE       :   int         =      0
							SCREAMING_SNAKE_CASE       :   Callable[[int], int]
							SCREAMING_SNAKE_CASE       :   int

							for poly in polynomials:
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      1
														while func(_a					) == poly(_a					):
																					x_val += 1

														ret += poly(_a					)

							return ret


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







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 _UpperCamelCase				(				__A				,						unittest.TestCase ):
			'''simple docstring'''







			pass






@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							List[str]:

				"""simple docstring"""

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							ort.SessionOptions()
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							False
				return options
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
				SCREAMING_SNAKE_CASE :   int					  =							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 :   Optional[Any]					  =							"A red cat sitting on a park bench"

				SCREAMING_SNAKE_CASE :   Tuple					  =							np.random.RandomState(0						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							pipe(
				    prompt=a   ,   image=a   ,   mask_image=a   ,   guidance_scale=7.5   ,   num_inference_steps=10   ,   generator=a   ,   output_type="np"   ,   )
				SCREAMING_SNAKE_CASE :   List[Any]					  =							output.images
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							images[0, 255:258, 255:258, -1]

				assert images.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   int					  =							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 				__UpperCamelCase			(      self				:			List[Any]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							LMSDiscreteScheduler.from_pretrained(
				    "runwayml/stable-diffusion-inpainting"   ,   subfolder="scheduler"   ,   revision="onnx"						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							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 :   List[Any]					  =							"A red cat sitting on a park bench"

				SCREAMING_SNAKE_CASE :   Dict					  =							np.random.RandomState(0						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							pipe(
				    prompt=a   ,   image=a   ,   mask_image=a   ,   guidance_scale=7.5   ,   num_inference_steps=20   ,   generator=a   ,   output_type="np"   ,   )
				SCREAMING_SNAKE_CASE :   List[str]					  =							output.images
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							images[0, 255:258, 255:258, -1]

				assert images.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   Any					  =							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