


import math

class   A__							:




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =x
										_SCREAMING_SNAKE_CASE	     =y
										_SCREAMING_SNAKE_CASE	     =step_size
										_SCREAMING_SNAKE_CASE	     =function_to_optimize




						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   int:


										"""simple docstring"""





										return self.function(self.x ,			self.y     )




						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =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    :					str     )							->   Optional[int]:


										"""simple docstring"""





										return hash(str(self     )     )




						def __eq__(				self    :					Tuple ,			_a    :					int     )							->   Union[str, Any]:


										"""simple docstring"""





										if isinstance(_UpperCAmelCase ,			_UpperCAmelCase     ):
														return hash(str(self     )     ) == hash(str(_UpperCAmelCase     )     )
										return False




						def __str__(				self    :					Optional[Any]     )							->   int:


										"""simple docstring"""





										return f"x: {self.x} y: {self.y}"







def   lowerCamelCase(      a__		,a__ = True		,a__ = math.inf		,a__ = -math.inf		,a__ = math.inf		,a__ = -math.inf		,a__ = False		,a__ = 1_0000		,):
				_SCREAMING_SNAKE_CASE	     =search_prob
				_SCREAMING_SNAKE_CASE	     =[]  # list to store the current score at each iteration
				_SCREAMING_SNAKE_CASE	     =0
				_SCREAMING_SNAKE_CASE	     =False
				_SCREAMING_SNAKE_CASE	     =set()
				while not solution_found and iterations < max_iter:
								visited.add(UpperCamelCase_)
								iterations += 1
								_SCREAMING_SNAKE_CASE	     =current_state.score()
								scores.append(UpperCamelCase_)
								_SCREAMING_SNAKE_CASE	     =current_state.get_neighbors()
								_SCREAMING_SNAKE_CASE	     =-math.inf
								_SCREAMING_SNAKE_CASE	     =math.inf
								_SCREAMING_SNAKE_CASE	     =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
												_SCREAMING_SNAKE_CASE	     =neighbor.score() - current_score
												if find_max:  # finding max
																# going to direction with greatest ascent
																if change > max_change and change > 0:
																				_SCREAMING_SNAKE_CASE	     =change
																				_SCREAMING_SNAKE_CASE	     =neighbor
												else:  # finding min
																# to direction with greatest descent
																if change < min_change and change < 0:
																				_SCREAMING_SNAKE_CASE	     =change
																				_SCREAMING_SNAKE_CASE	     =neighbor
								if next_state is not None:
												# we found at least one neighbor which improved the current state
												_SCREAMING_SNAKE_CASE	     =next_state
								else:
												# since we have no neighbor that improves the solution we stop the search
												_SCREAMING_SNAKE_CASE	     =True

				if visualization:
								from matplotlib import pyplot as plt

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

				return current_state


if __name__ == "__main__":
							import doctest

							doctest.testmod()







							def   lowerCamelCase(      a__		,a__):
											return (x**2) + (y**2)

							# starting the problem with initial coordinates (3, 4)
							snake_case_  :	List[Any]							  =							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							snake_case_  :	Optional[Any]							  =							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)
							snake_case_  :	Optional[int]							  =							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
							snake_case_  :	Optional[Any]							  =							hill_climbing(
							    prob, find_max=False, max_x=1_00, 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   lowerCamelCase(      a__		,a__):
											return (3 * x**2) - (6 * y)

							snake_case_  :	Dict							  =							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							snake_case_  :	int							  =							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()}"""
							)





import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()
class 				__snake_case     (unittest.TestCase						):
				lowerCAmelCase__			=   "hf-internal-testing/tiny-stable-diffusion-pipe"
				lowerCAmelCase__			=   1
				lowerCAmelCase__			=   7_7
				lowerCAmelCase__			=   3_2
				lowerCAmelCase__			=   4
				lowerCAmelCase__			=   lowerCAmelCase__			=   6_4
				lowerCAmelCase__			=   torch.manual_seed(0						)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]						,		_UpperCAmelCase					:						Optional[Any]=0.9999					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					UNetaDConditionModel.from_pretrained(self.model_id						,		subfolder="""unet"""					)
								_lowerCAmelCase			:			Optional[Any]        =					unet.to(_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					EMAModel(unet.parameters()						,		decay=_UpperCAmelCase						,		model_cls=_UpperCAmelCase						,		model_config=unet.config					)
								return unet, ema_unet



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase			:			Any        =					torch.randn(
								    self.batch_size						,		self.num_in_channels						,		self.latent_height						,		self.latent_width						,		generator=self.generator					).to(_UpperCAmelCase					)
								_lowerCAmelCase			:			List[str]        =					torch.randint(0						,		1000						,		size=(self.batch_size,)						,		generator=self.generator					).to(_UpperCAmelCase					)
								_lowerCAmelCase			:			Any        =					torch.randn(
								    self.batch_size						,		self.prompt_length						,		self.text_encoder_hidden_dim						,		generator=self.generator					).to(_UpperCAmelCase					)
								return noisy_latents, timesteps, encoder_hidden_states



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						Dict					)     ->			Dict:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					{}
								for k, param in unet.state_dict().items():
												_lowerCAmelCase			:			List[Any]        =					torch.randn_like(_UpperCAmelCase					) + (param * torch.randn_like(_UpperCAmelCase					))
												updated_state_dict.update({k: updated_param}					)
								unet.load_state_dict(_UpperCAmelCase					)
								return unet



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			Optional[int]        =					self.get_models()
								# Take the first (hypothetical) EMA step.
								ema_unet.step(unet.parameters()					)
								assert ema_unet.optimization_step == 1

								# Take two more.
								for _ in range(2					):
												ema_unet.step(unet.parameters()					)
								assert ema_unet.optimization_step == 3



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]					)     ->			str:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			List[Any]        =					self.get_models()
								# Since the `unet` is not being updated (i.e., backprop'd)
								# there won't be any difference between the `params` of `unet`
								# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
								ema_unet.step(unet.parameters()					)
								_lowerCAmelCase			:			Optional[int]        =					list(unet.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params						,		_UpperCAmelCase					):
												assert torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)

								# The above holds true even if we call `ema.step()` multiple times since
								# `unet` params are still not being updated.
								for _ in range(4					):
												ema_unet.step(unet.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params						,		_UpperCAmelCase					):
												assert torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						str					)     ->			Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			Union[str, Any]        =					self.get_models()
								# Here we simulate the parameter updates for `unet`. Since there might
								# be some parameters which are initialized to zero we take extra care to
								# initialize their values to something non-zero before the multiplication.
								_lowerCAmelCase			:			str        =					self.simulate_backprop(_UpperCAmelCase					)

								# Take the EMA step.
								ema_unet.step(unet_pseudo_updated_step_one.parameters()					)

								# Now the EMA'd parameters won't be equal to the original model parameters.
								_lowerCAmelCase			:			Tuple        =					list(unet_pseudo_updated_step_one.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params						,		_UpperCAmelCase					):
												assert ~torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)

								# Ensure this is the case when we take multiple EMA steps.
								for _ in range(4					):
												ema_unet.step(unet.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params						,		_UpperCAmelCase					):
												assert ~torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			str        =					self.get_models()

								# First backprop + EMA
								_lowerCAmelCase			:			Optional[int]        =					self.simulate_backprop(_UpperCAmelCase					)
								ema_unet.step(unet_step_one.parameters()					)
								_lowerCAmelCase			:			List[str]        =					ema_unet.shadow_params

								# Second backprop + EMA
								_lowerCAmelCase			:			Optional[int]        =					self.simulate_backprop(_UpperCAmelCase					)
								ema_unet.step(unet_step_two.parameters()					)
								_lowerCAmelCase			:			Tuple        =					ema_unet.shadow_params

								for step_one, step_two in zip(_UpperCAmelCase						,		_UpperCAmelCase					):
												assert ~torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]					)     ->			str:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			int        =					self.get_models(decay=0.0					)
								_lowerCAmelCase			:			Dict        =					self.simulate_backprop(_UpperCAmelCase					)
								ema_unet.step(unet_step_one.parameters()					)
								_lowerCAmelCase			:			Any        =					ema_unet.shadow_params

								_lowerCAmelCase			:			List[Any]        =					self.simulate_backprop(_UpperCAmelCase					)
								ema_unet.step(unet_step_two.parameters()					)
								_lowerCAmelCase			:			int        =					ema_unet.shadow_params

								for step_one, step_two in zip(_UpperCAmelCase						,		_UpperCAmelCase					):
												assert torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)



				@skip_mps
				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			str:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			Tuple        =					self.get_models()
								_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase			:			Tuple        =					self.get_dummy_inputs()

								with tempfile.TemporaryDirectory() as tmpdir:
												ema_unet.save_pretrained(_UpperCAmelCase					)
												_lowerCAmelCase			:			Union[str, Any]        =					UNetaDConditionModel.from_pretrained(_UpperCAmelCase						,		model_cls=_UpperCAmelCase					)
												_lowerCAmelCase			:			Any        =					loaded_unet.to(unet.device					)

								# Since no EMA step has been performed the outputs should match.
								_lowerCAmelCase			:			str        =					unet(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase					).sample
								_lowerCAmelCase			:			Union[str, Any]        =					loaded_unet(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase					).sample

								assert torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase						,		atol=1E-4					)

