

"""simple docstring"""

import math


class 			_snake_case     :







						def __init__(self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						List[str]:
											__lowercase												=			x
											__lowercase												=			y
											__lowercase												=			step_size
											__lowercase												=			function_to_optimize







						def        _UpperCamelCase    (self							)			->						int:
											return self.function(self.x     ,							self.y							)







						def        _UpperCamelCase    (self							)			->						List[str]:
											__lowercase												=			self.step_size
											return [
											    SearchProblem(UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							self.function							)
											    for x, y in (
											        (self.x - step_size, self.y - step_size),
											        (self.x - step_size, self.y),
											        (self.x - step_size, self.y + step_size),
											        (self.x, self.y - step_size),
											        (self.x, self.y + step_size),
											        (self.x + step_size, self.y - step_size),
											        (self.x + step_size, self.y),
											        (self.x + step_size, self.y + step_size),
											    )
											]







						def __hash__(self							)			->						Optional[Any]:
											return hash(str(self							)							)







						def __eq__(self     ,							UpperCAmelCase_							)			->						Any:
											if isinstance(UpperCAmelCase_     ,							UpperCAmelCase_							):
																return hash(str(self							)							) == hash(str(UpperCAmelCase_							)							)
											return False







						def __str__(self							)			->						Optional[int]:
											return f'''x: {self.x} y: {self.y}'''





def 				__UpperCamelCase   (    a__: List[str] ,	a__: bool = True ,	a__: float = math.inf ,	a__: float = -math.inf ,	a__: float = math.inf ,	a__: float = -math.inf ,	a__: bool = False ,	a__: int = 1_00_00 ,	)				->			SearchProblem:




					"""simple docstring"""



					__lowercase												=			search_prob
					__lowercase												=			[]  # list to store the current score at each iteration
					__lowercase												=			0
					__lowercase												=			False
					__lowercase												=			set()
					while not solution_found and iterations < max_iter:
										visited.add(a__			)
										iterations += 1
										__lowercase												=			current_state.score()
										scores.append(a__			)
										__lowercase												=			current_state.get_neighbors()
										__lowercase												=			-math.inf
										__lowercase												=			math.inf
										__lowercase												=			None  # to hold the next best neighbor
										for neighbor in neighbors:
															if neighbor in visited:
																				continue  # do not want to visit the same state again
															if (
															    neighbor.x > max_x
															    or neighbor.x < min_x
															    or neighbor.y > max_y
															    or neighbor.y < min_y
															):
																				continue  # neighbor outside our bounds
															__lowercase												=			neighbor.score() - current_score
															if find_max:  # finding max
																				# going to direction with greatest ascent
																				if change > max_change and change > 0:
																									__lowercase												=			change
																									__lowercase												=			neighbor
															else:  # finding min
																				# to direction with greatest descent
																				if change < min_change and change < 0:
																									__lowercase												=			change
																									__lowercase												=			neighbor
										if next_state is not None:
															# we found at least one neighbor which improved the current state
															__lowercase												=			next_state
										else:
															# since we have no neighbor that improves the solution we stop the search
															__lowercase												=			True

					if visualization:
										from matplotlib import pyplot as plt

										plt.plot(range(a__			) ,	a__			)
										plt.xlabel('''Iterations'''			)
										plt.ylabel('''Function values'''			)
										plt.show()

					return current_state


if __name__ == "__main__":
							import doctest

							doctest.testmod()





							def 				__UpperCamelCase   (    a__: Union[str, Any] ,	a__: List[Any]			)				->			Tuple:




												"""simple docstring"""



												return (x**2) + (y**2)

							# starting the problem with initial coordinates (3, 4)
							a__ :	List[Any]						= SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							a__ :	Tuple						= hill_climbing(prob, find_max=False)
							print(
							    """The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: """
							    f"""{local_min.score()}"""
							)

							# starting the problem with initial coordinates (12, 47)
							a__ :	str						= SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
							a__ :	int						= hill_climbing(
							    prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
							)
							print(
							    """The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
							    f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
							)





							def 				__UpperCamelCase   (    a__: Tuple ,	a__: Any			)				->			str:




												"""simple docstring"""



												return (3 * x**2) - (6 * y)

							a__ :	Any						= SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							a__ :	Tuple						= hill_climbing(prob, find_max=True)


							print(
							    """The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: """
							    f"""{local_min.score()}"""
							)





"""simple docstring"""

from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


a__ :	int						= logging.get_logger(__name__)  # pylint: disable=invalid-name


class 			_snake_case     (						UpperCamelCase       ):
						__snake_case 			=     ['safety_checker', 'feature_extractor']







						def __init__(self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = True     ,							)			->						Optional[int]:
											super().__init__()
											if hasattr(scheduler.config     ,							'''steps_offset'''							) and scheduler.config.steps_offset != 1:
																__lowercase												=			(
																    f'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
																    f''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
																    '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
																    ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
																    ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
																    ''' file'''
																)
																deprecate('''steps_offset!=1'''     ,							'''1.0.0'''     ,							UpperCAmelCase_     ,							standard_warn=UpperCAmelCase_							)
																__lowercase												=			dict(scheduler.config							)
																__lowercase												=			1
																__lowercase												=			FrozenDict(UpperCAmelCase_							)

											self.register_modules(
											    vae=UpperCAmelCase_     ,							text_encoder=UpperCAmelCase_     ,							tokenizer=UpperCAmelCase_     ,							unet=UpperCAmelCase_     ,							scheduler=UpperCAmelCase_     ,							safety_checker=UpperCAmelCase_     ,							feature_extractor=UpperCAmelCase_     ,							)
											self.register_to_config(requires_safety_checker=UpperCAmelCase_							)







						@property
						def        _UpperCamelCase    (self							)			->						Dict[str, Any]:
											return {k: getattr(self     ,							UpperCAmelCase_							) for k in self.config.keys() if not k.startswith('''_'''							)}







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_ = "auto"							)			->						str:
											if slice_size == "auto":
																# half the attention head size is usually a good trade-off between
																# speed and memory
																__lowercase												=			self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(UpperCAmelCase_							)







						def        _UpperCamelCase    (self							)			->						int:
											self.enable_attention_slicing(UpperCAmelCase_							)







						@torch.no_grad()
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = 0.8     ,							UpperCAmelCase_ = 5_0     ,							UpperCAmelCase_ = 7.5     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							UpperCAmelCase_ = 0.0     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = "pil"     ,							UpperCAmelCase_ = True     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							)			->						str:
											# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
											return StableDiffusionInpaintPipelineLegacy(**self.components							)(
											    prompt=UpperCAmelCase_     ,							image=UpperCAmelCase_     ,							mask_image=UpperCAmelCase_     ,							strength=UpperCAmelCase_     ,							num_inference_steps=UpperCAmelCase_     ,							guidance_scale=UpperCAmelCase_     ,							negative_prompt=UpperCAmelCase_     ,							num_images_per_prompt=UpperCAmelCase_     ,							eta=UpperCAmelCase_     ,							generator=UpperCAmelCase_     ,							output_type=UpperCAmelCase_     ,							return_dict=UpperCAmelCase_     ,							callback=UpperCAmelCase_     ,							)







						@torch.no_grad()
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = 0.8     ,							UpperCAmelCase_ = 5_0     ,							UpperCAmelCase_ = 7.5     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							UpperCAmelCase_ = 0.0     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = "pil"     ,							UpperCAmelCase_ = True     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							**UpperCAmelCase_     ,							)			->						List[str]:
											# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
											return StableDiffusionImgaImgPipeline(**self.components							)(
											    prompt=UpperCAmelCase_     ,							image=UpperCAmelCase_     ,							strength=UpperCAmelCase_     ,							num_inference_steps=UpperCAmelCase_     ,							guidance_scale=UpperCAmelCase_     ,							negative_prompt=UpperCAmelCase_     ,							num_images_per_prompt=UpperCAmelCase_     ,							eta=UpperCAmelCase_     ,							generator=UpperCAmelCase_     ,							output_type=UpperCAmelCase_     ,							return_dict=UpperCAmelCase_     ,							callback=UpperCAmelCase_     ,							callback_steps=UpperCAmelCase_     ,							)







						@torch.no_grad()
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = 5_1_2     ,							UpperCAmelCase_ = 5_1_2     ,							UpperCAmelCase_ = 5_0     ,							UpperCAmelCase_ = 7.5     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							UpperCAmelCase_ = 0.0     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = "pil"     ,							UpperCAmelCase_ = True     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							)			->						Optional[int]:
											# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
											return StableDiffusionPipeline(**self.components							)(
											    prompt=UpperCAmelCase_     ,							height=UpperCAmelCase_     ,							width=UpperCAmelCase_     ,							num_inference_steps=UpperCAmelCase_     ,							guidance_scale=UpperCAmelCase_     ,							negative_prompt=UpperCAmelCase_     ,							num_images_per_prompt=UpperCAmelCase_     ,							eta=UpperCAmelCase_     ,							generator=UpperCAmelCase_     ,							latents=UpperCAmelCase_     ,							output_type=UpperCAmelCase_     ,							return_dict=UpperCAmelCase_     ,							callback=UpperCAmelCase_     ,							callback_steps=UpperCAmelCase_     ,							)


