
"""simple docstring"""







from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = "x"							,    SCREAMING_SNAKE_CASE = 10**-10							,    SCREAMING_SNAKE_CASE = 1							,    )  ->     complex:







							"""simple docstring"""


							__snake_case     			=       symbols(SCREAMING_SNAKE_CASE )
							__snake_case     			=       lambdify(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							__snake_case     			=       lambdify(SCREAMING_SNAKE_CASE							,    diff(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) )

							__snake_case     			=       starting_point

							while True:
														if diff_function(SCREAMING_SNAKE_CASE ) != 0:
																					__snake_case     			=       prev_guess - multiplicity * func(SCREAMING_SNAKE_CASE ) / diff_function(
																					    SCREAMING_SNAKE_CASE )
														else:
																					raise ZeroDivisionError("Could not find root" ) from None

														# Precision is checked by comparing the difference of consecutive guesses
														if abs(next_guess - prev_guess ) < precision:
																					return next_guess

														__snake_case     			=       next_guess


# Let's Execute
if __name__ == "__main__":
		# Find root of trigonometric function
		# Find value of pi
		print(F"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")

		# Find root of polynomial
		# Find fourth Root of 5
		print(F"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}""")

		# Find value of e
		print(
		    """The root of log(y) - 1 = 0 is """,
		    F"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
		)

		# Exponential Roots
		print(
		    """The root of exp(x) - 1 = 0 is""",
		    F"""{newton_raphson('exp(x) - 1', 10, precision=0.005)}""",
		)

		# Find root of cos(x)
		print(F"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")


"""simple docstring"""







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 							__magic_name__     (		unittest.TestCase   ):





					@property
					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												return (
												    "CUDAExecutionProvider",
												    {
												        "gpu_mem_limit": "15000000000",  # 15GB
												        "arena_extend_strategy": "kSameAsRequested",
												    },
												)





					@property
					def 							lowerCAmelCase      (				self   :							str    ):
												__snake_case     			=       ort.SessionOptions()
												__snake_case     			=       False
												return options





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__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     			=       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
												__snake_case     			=       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_    )

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

												__snake_case     			=       np.random.RandomState(0    )
												__snake_case     			=       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"							,						)

												__snake_case     			=       output.images[0]

												assert image.shape == (512, 512, 3)
												assert np.abs(expected_image - image    ).max() < 1e-2
