

from math import factorial






class _UpperCamelCase				:
			'''simple docstring'''







			def __init__(      self				:			Dict   ,   a				:			Optional[int]   ,   a				:			Dict						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							real
				if isinstance(a   ,   a						):
					SCREAMING_SNAKE_CASE :   Dict					  =							[1] * rank
				else:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							rank
			def __repr__(      self				:			List[Any]						)				->							str:

				"""simple docstring"""

				return (
				    F"{self.real}+"
				    F"{'+'.join(str(a						)+'E'+str(n+1						)for n,dual in enumerate(self.duals						)						)}"
				)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self.duals.copy()
				while cur[-1] == 0:
					cur.pop(-1						)
				return Dual(self.real   ,   a						)
			def __add__(      self				:			Tuple   ,   a				:			List[str]						)				->							List[str]:

				"""simple docstring"""

				if not isinstance(a   ,   a						):
					return Dual(self.real + other   ,   self.duals						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							self.duals.copy()
				SCREAMING_SNAKE_CASE :   Dict					  =							other.duals.copy()
				if len(a						) > len(a						):
					o_dual.extend([1] * (len(a						) - len(a						))						)
				elif len(a						) < len(a						):
					s_dual.extend([1] * (len(a						) - len(a						))						)
				SCREAMING_SNAKE_CASE :   int					  =							[]
				for i in range(len(a						)						):
					new_duals.append(s_dual[i] + o_dual[i]						)
				return Dual(self.real + other.real   ,   a						)

			lowerCamelCase__							     =__add__
			def __sub__(      self				:			Optional[int]   ,   a				:			int						)				->							Dict:

				"""simple docstring"""

				return self + other * -1
			def __mul__(      self				:			Union[str, Any]   ,   a				:			int						)				->							int:

				"""simple docstring"""

				if not isinstance(a   ,   a						):
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							[]
					for i in self.duals:
						new_duals.append(i * other						)
					return Dual(self.real * other   ,   a						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							[0] * (len(self.duals						) + len(other.duals						) + 1)
				for i, item in enumerate(self.duals						):
					for j, jtem in enumerate(other.duals						):
						new_duals[i + j + 1] += item * jtem
				for k in range(len(self.duals						)						):
					new_duals[k] += self.duals[k] * other.real
				for index in range(len(other.duals						)						):
					new_duals[index] += other.duals[index] * self.real
				return Dual(self.real * other.real   ,   a						)

			lowerCamelCase__							     =__mul__
			def __truediv__(      self				:			int   ,   a				:			int						)				->							Optional[int]:

				"""simple docstring"""

				if not isinstance(a   ,   a						):
					SCREAMING_SNAKE_CASE :   int					  =							[]
					for i in self.duals:
						new_duals.append(i / other						)
					return Dual(self.real / other   ,   a						)
				raise ValueError
			def __floordiv__(      self				:			List[Any]   ,   a				:			Optional[int]						)				->							List[Any]:

				"""simple docstring"""

				if not isinstance(a   ,   a						):
					SCREAMING_SNAKE_CASE :   int					  =							[]
					for i in self.duals:
						new_duals.append(i // other						)
					return Dual(self.real // other   ,   a						)
				raise ValueError






			def __pow__(      self				:			Optional[Any]   ,   a				:			Dict						)				->							Optional[Any]:

				"""simple docstring"""

				if n < 0 or isinstance(a   ,   a						):
					raise ValueError("power must be a positive integer"						)
				if n == 0:
					return 1
				if n == 1:
					return self
				SCREAMING_SNAKE_CASE :   List[str]					  =							self
				for _ in range(n - 1						):
					x *= self
				return x

def 			lowerCamelCase__			(       _a				,      _a				,      _a):
	if not callable(_a):
		raise ValueError("differentiate() requires a function as input for func")
	if not isinstance(_a				,      (float, int)):
		raise ValueError("differentiate() requires a float as input for position")
	if not isinstance(_a				,      _a):
		raise ValueError("differentiate() requires an int as input for order")
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							Dual(_a				,      1)
	SCREAMING_SNAKE_CASE :   List[str]					  =							func(_a)
	if order == 0:
		return result.real
	return result.duals[order - 1] * factorial(_a)


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			def 			lowerCamelCase__			(       _a):
				return y**2 * y**4


			print(differentiate(f, 9, 2))


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