code: 
  
  
  
  
  """simple docstring"""
  
  import warnings
  from collections.abc import Callable
  from typing import Any
  
  import numpy
  from matplotlib import pyplot
  
  __A 		=			0.25 + 0.0j
  __A 		=			-0.4 + 0.6j
  __A 		=			-0.1 + 0.6_51j
  __A 		=			-2.0
  __A 		=			56
  __A 		=			2.0
  __A 		=			666
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     complex       ,     lowercase__  :     numpy.ndarray   ):
  		return numpy.exp(UpperCamelCase__   ) + c_parameter
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     complex       ,     lowercase__  :     numpy.ndarray   ):
  		return z_values * z_values + c_parameter
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     float       ,     lowercase__  :     int   ):
  		snake_case		:						Tuple								=				numpy.linspace(-window_size       ,     UpperCamelCase__       ,     UpperCamelCase__   )
  		snake_case		:						Any								=				x.reshape((nb_pixels, 1)   )
  		snake_case		:						Union[str, Any]								=				numpy.linspace(-window_size       ,     UpperCamelCase__       ,     UpperCamelCase__   )
  		snake_case		:						List[str]								=				y.reshape((1, nb_pixels)   )
  		return x + 1.0j * y
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Callable[[Any, numpy.ndarray], numpy.ndarray]       ,     lowercase__  :     Any       ,     lowercase__  :     int       ,     lowercase__  :     numpy.ndarray       ,     lowercase__  :     float | None = None       ,     ):
  		snake_case		:						Dict								=				z_a.astype("complex64"   )
  		for _ in range(UpperCamelCase__   ):
  				snake_case		:						Optional[Any]								=				eval_function(UpperCamelCase__       ,     UpperCamelCase__   )
  				if infinity is not None:
  						numpy.nan_to_num(UpperCamelCase__       ,     copy=UpperCamelCase__       ,     nan=UpperCamelCase__   )
  						snake_case		:						List[Any]								=				infinity
  		return z_n
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     str       ,     lowercase__  :     Any       ,     lowercase__  :     float       ,     lowercase__  :     numpy.ndarray       ,     ):
  		snake_case		:						int								=				(abs(UpperCamelCase__   )).transpose()
  		snake_case		:						str								=				abs_z_final[::-1, :]
  		pyplot.matshow(abs_z_final < escape_radius   )
  		pyplot.title(F'''Julia set of ${function_label}$, $c={function_params}$'''   )
  		pyplot.show()
  
  
  
  def   UpperCamelCase__     (		):
  		warnings.filterwarnings(
  		    "ignore"       ,     category=UpperCamelCase__       ,     message="overflow encountered in multiply"   )
  		warnings.filterwarnings(
  		    "ignore"       ,     category=UpperCamelCase__       ,     message="invalid value encountered in multiply"       ,     )
  		warnings.filterwarnings(
  		    "ignore"       ,     category=UpperCamelCase__       ,     message="overflow encountered in absolute"   )
  		warnings.filterwarnings(
  		    "ignore"       ,     category=UpperCamelCase__       ,     message="overflow encountered in exp"   )
  
  
  if __name__ == "__main__":
  							__A 		=			prepare_grid(window_size, nb_pixels)
  
  							ignore_overflow_warnings()  # See file header for explanations
  
  							__A 		=			24
  							__A 		=			2 * abs(c_cauliflower) + 1
  							__A 		=			iterate_function(
  							    eval_quadratic_polynomial,
  							    c_cauliflower,
  							    nb_iterations,
  							    z_a,
  							    infinity=1.1 * escape_radius,
  							)
  							show_results("z^2+c", c_cauliflower, escape_radius, z_final)
  
  							__A 		=			64
  							__A 		=			2 * abs(c_polynomial_a) + 1
  							__A 		=			iterate_function(
  							    eval_quadratic_polynomial,
  							    c_polynomial_a,
  							    nb_iterations,
  							    z_a,
  							    infinity=1.1 * escape_radius,
  							)
  							show_results("z^2+c", c_polynomial_a, escape_radius, z_final)
  
  							__A 		=			161
  							__A 		=			2 * abs(c_polynomial_a) + 1
  							__A 		=			iterate_function(
  							    eval_quadratic_polynomial,
  							    c_polynomial_a,
  							    nb_iterations,
  							    z_a,
  							    infinity=1.1 * escape_radius,
  							)
  							show_results("z^2+c", c_polynomial_a, escape_radius, z_final)
  
  							__A 		=			12
  							__A 		=			10_000.0
  							__A 		=			iterate_function(
  							    eval_exponential,
  							    c_exponential,
  							    nb_iterations,
  							    z_a + 2,
  							    infinity=1.0e1_0,
  							)
  							show_results("e^z+c", c_exponential, escape_radius, z_final)
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
  from diffusers.utils.testing_utils import (
      enable_full_determinism,
      load_image,
      load_numpy,
      nightly,
      require_torch_gpu,
      skip_mps,
      torch_device,
  )
  
  from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  class 			lowerCamelCase__    (  lowerCamelCase_				,  unittest.TestCase			):
  			a__ :      Optional[Any]								=     RePaintPipeline
  			a__ :      List[Any]								=     IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
  			a__ :      List[Any]								=     PipelineTesterMixin.required_optional_params - {
  			    """latents""",
  			    """num_images_per_prompt""",
  			    """callback""",
  			    """callback_steps""",
  			}
  			a__ :      List[Any]								=     IMAGE_INPAINTING_BATCH_PARAMS
  			a__ :      List[str]								=     False
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					torch.manual_seed(0							)
  					torch.manual_seed(0							)
  					snake_case		:						Union[str, Any]								=				UNetaDModel(
  					    block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=3				, out_channels=3				, down_block_types=("DownBlock2D", "AttnDownBlock2D")				, up_block_types=("AttnUpBlock2D", "UpBlock2D")				, )
  					snake_case		:						Union[str, Any]								=				RePaintScheduler()
  					snake_case		:						Optional[Any]								=				{"unet": unet, "scheduler": scheduler}
  					return components
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=0							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if str(SCREAMING_SNAKE_CASE							).startswith("mps"							):
  							snake_case		:						str								=				torch.manual_seed(SCREAMING_SNAKE_CASE							)
  					else:
  							snake_case		:						List[Any]								=				torch.Generator(device=SCREAMING_SNAKE_CASE							).manual_seed(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Any								=				np.random.RandomState(SCREAMING_SNAKE_CASE							).standard_normal((1, 3, 32, 32)							)
  					snake_case		:						Any								=				torch.from_numpy(SCREAMING_SNAKE_CASE							).to(device=SCREAMING_SNAKE_CASE				, dtype=torch.floataa							)
  					snake_case		:						List[str]								=				(image > 0).to(device=SCREAMING_SNAKE_CASE				, dtype=torch.floataa							)
  					snake_case		:						int								=				{
  					    "image": image,
  					    "mask_image": mask,
  					    "generator": generator,
  					    "num_inference_steps": 5,
  					    "eta": 0.0,
  					    "jump_length": 2,
  					    "jump_n_sample": 2,
  					    "output_type": "numpy",
  					}
  					return inputs
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						List[Any]								=				"cpu"  # ensure determinism for the device-dependent torch.Generator
  					snake_case		:						Any								=				self.get_dummy_components()
  					snake_case		:						Optional[int]								=				RePaintPipeline(**SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[Any]								=				sd_pipe.to(SCREAMING_SNAKE_CASE							)
  					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Any								=				self.get_dummy_inputs(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Dict								=				sd_pipe(**SCREAMING_SNAKE_CASE							).images
  					snake_case		:						Optional[int]								=				image[0, -3:, -3:, -1]
  
  					assert image.shape == (1, 32, 32, 3)
  					snake_case		:						List[str]								=				np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]							)
  
  					assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3
  
  
  			@skip_mps
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_save_load_local()
  
  
  			@unittest.skip("non-deterministic pipeline"							)
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_inference_batch_single_identical()
  
  
  			@skip_mps
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_dict_tuple_outputs_equivalent()
  
  
  			@skip_mps
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_save_load_optional_components()
  
  
  
  			@skip_mps
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return super().test_attention_slicing_forward_pass()
  
  
  
  
  
  
  
  @nightly
  @require_torch_gpu
  class 			lowerCamelCase__    (  unittest.TestCase			):
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					super().tearDown()
  					gc.collect()
  					torch.cuda.empty_cache()
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[int]								=				load_image(
  					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
  					    "repaint/celeba_hq_256.png"							)
  					snake_case		:						Any								=				load_image(
  					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"							)
  					snake_case		:						List[Any]								=				load_numpy(
  					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
  					    "repaint/celeba_hq_256_result.npy"							)
  
  					snake_case		:						str								=				"google/ddpm-ema-celebahq-256"
  					snake_case		:						Optional[Any]								=				UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[int]								=				RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				RePaintPipeline(unet=SCREAMING_SNAKE_CASE				, scheduler=SCREAMING_SNAKE_CASE							).to(SCREAMING_SNAKE_CASE							)
  					repaint.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE							)
  					repaint.enable_attention_slicing()
  
  					snake_case		:						Union[str, Any]								=				torch.manual_seed(0							)
  					snake_case		:						int								=				repaint(
  					    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, num_inference_steps=250				, eta=0.0				, jump_length=10				, jump_n_sample=10				, generator=SCREAMING_SNAKE_CASE				, output_type="np"				, )
  					snake_case		:						List[Any]								=				output.images[0]
  
  					assert image.shape == (256, 256, 3)
  					assert np.abs(expected_image - image							).mean() < 1E-2
  
  