



"""simple docstring"""






import math as m






def 	snake_case__  (						__lowerCamelCase							:       Dict				,						__lowerCamelCase							:       Union[str, Any]				,						__lowerCamelCase							:       Tuple=0.0_01      ):
			"""simple docstring"""
			return (f(a + h      ) - f(a - h      )) / (2 * h)






def 	snake_case__  (						__lowerCamelCase							:       int				,						__lowerCamelCase							:       Union[str, Any]=0				,						__lowerCamelCase							:       Tuple=100				,						__lowerCamelCase							:       int=0.00_01				,						__lowerCamelCase							:       Tuple=1e-6				,						__lowerCamelCase							:       Union[str, Any]=False      ):
			"""simple docstring"""
			lowerCamelCase__						:						Tuple						      =xa  # set the initial guess
			lowerCamelCase__						:						Dict						      =[a]
			lowerCamelCase__						:						Any						      =abs(f(__lowerCamelCase      )      )
			lowerCamelCase__						:						Optional[int]						      =lambda __lowerCamelCase      : calc_derivative(__lowerCamelCase				,						__lowerCamelCase				,						h=__lowerCamelCase      )  # noqa: E731  Derivative of f(x)
			for _ in range(__lowerCamelCase      ):
						if fa(__lowerCamelCase      ) == 0:
									raise ValueError('''No converging solution found'''      )
						lowerCamelCase__						:						Tuple						      =a - f(__lowerCamelCase      ) / fa(__lowerCamelCase      )  # Calculate the next estimate
						if logsteps:
									steps.append(__lowerCamelCase      )
						if error < maxerror:
									break
			else:
						raise ValueError('''Iteration limit reached, no converging solution found'''      )
			if logsteps:
						# If logstep is true, then log intermediate steps
						return a, error, steps
			return a, error


if __name__ == "__main__":
		from matplotlib import pyplot as plt

		_lowercase					:					Optional[int] 		=		lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
		_lowercase		,      _lowercase		,      _lowercase					:					Union[str, Any] 		=		newton_raphson(
		    f, xa=1_0, maxiter=1_0_0_0, step=1E-6, logsteps=True
		)
		plt.plot([abs(f(x)) for x in steps])
		plt.xlabel("step")
		plt.ylabel("error")
		plt.show()
		print(f'solution = {{{solution:f}}}, error = {{{error:f}}}')





"""simple docstring"""

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__lowercase    =				False






@nightly
@require_torch_gpu
class 		_lowercase (						unittest.TestCase      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[str]				)				->				List[Any]:
													'''simple docstring'''



													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()





						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
													# remove text_unet
													pipe.remove_unused_weights()
													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							=load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)

													__UpperCamelCase							=torch.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt='''first prompt'''     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=2     , output_type='''numpy'''     , ).images

													with tempfile.TemporaryDirectory() as tmpdirname:
																				pipe.save_pretrained(UpperCamelCase__				)
																				__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCamelCase__				)

													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							=generator.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt='''first prompt'''     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=2     , output_type='''numpy'''     , ).images

													assert np.abs(image - new_image				).sum() < 1E-5, "Models don't have the same forward pass"





						def   UpperCAmelCase_ (				self							:    Optional[int]				)				->				Optional[int]:
													'''simple docstring'''



													__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
													pipe.remove_unused_weights()
													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							='''cyberpunk 2077'''
													__UpperCamelCase							=load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
													__UpperCamelCase							=torch.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt=UpperCamelCase__     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=50     , output_type='''numpy'''     , ).images

													__UpperCamelCase							=image[0, 253:256, 253:256, -1]

													assert image.shape == (1, 512, 512, 3)
													__UpperCamelCase							=np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]				)

													assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2



