



import math
import random
from typing import Any

from .hill_climbing import SearchProblem






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase = True	,				__UpperCamelCase = math.inf	,				__UpperCamelCase = -math.inf	,				__UpperCamelCase = math.inf	,				__UpperCamelCase = -math.inf	,				__UpperCamelCase = False	,				__UpperCamelCase = 1_00	,				__UpperCamelCase = 0.0_1	,				__UpperCamelCase = 1	,				)	->						Any:
				a__	:							Optional[int]      						= False
				a__	:							str      						= search_prob
				a__	:							Union[str, Any]      						= start_temperate
				a__	:							Dict      						= []
				a__	:							Dict      						= 0
				a__	:							int      						= None

				while not search_end:
								a__	:							Optional[Any]      						= current_state.score()
								if best_state is None or current_score > best_state.score():
												a__	:							str      						= current_state
								scores.append(__UpperCamelCase )
								iterations += 1
								a__	:							Dict      						= None
								a__	:							Dict      						= current_state.get_neighbors()
								while (
								    next_state is None and neighbors
								):  # till we do not find a neighbor that we can move to
												a__	:							Any      						= random.randint(0	,				len(__UpperCamelCase ) - 1 )  # picking a random neighbor
												a__	:							Union[str, Any]      						= neighbors.pop(__UpperCamelCase )
												a__	:							Optional[int]      						= picked_neighbor.score() - current_score

												if (
												    picked_neighbor.x > max_x
												    or picked_neighbor.x < min_x
												    or picked_neighbor.y > max_y
												    or picked_neighbor.y < min_y
												):
																continue  # neighbor outside our bounds

												if not find_max:
																a__	:							str      						= change * -1  # in case we are finding minimum
												if change > 0:  # improves the solution
																a__	:							List[Any]      						= picked_neighbor
												else:
																a__	:							Optional[int]      						= (math.e) ** (
																    change / current_temp
																)  # probability generation function
																if random.random() < probability:  # random number within probability
																				a__	:							List[str]      						= picked_neighbor
								a__	:							Optional[Any]      						= current_temp - (current_temp * rate_of_decrease)

								if current_temp < threshold_temp or next_state is None:
												# temperature below threshold, or could not find a suitable neighbor
												a__	:							int      						= True
								else:
												a__	:							int      						= next_state

				if visualization:
								from matplotlib import pyplot as plt

								plt.plot(range(__UpperCamelCase )	,				__UpperCamelCase )
								plt.xlabel("Iterations" )
								plt.ylabel("Function values" )
								plt.show()
				return best_state


if __name__ == "__main__":






						def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						Dict:
										return (x**2) + (y**2)

						# starting the problem with initial coordinates (12, 47)
						lowerCamelCase		       =      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
						lowerCamelCase		       =      simulated_annealing(
						    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()}'
						)

						# starting the problem with initial coordinates (12, 47)
						lowerCamelCase		       =      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
						lowerCamelCase		       =      simulated_annealing(
						    prob, find_max=True, max_x=1_00, min_x=5, max_y=50, min_y=-5, visualization=True
						)
						print(
						    """The maximum 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     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						Tuple:
										return (3 * x**2) - (6 * y)

						lowerCamelCase		       =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
						lowerCamelCase		       =      simulated_annealing(prob, find_max=False, visualization=True)
						print(
						    """The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
						    F'{local_min.score()}'
						)

						lowerCamelCase		       =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
						lowerCamelCase		       =      simulated_annealing(prob, find_max=True, visualization=True)






						print(
						    """The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
						    F'{local_min.score()}'
						)







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							@property
							def 		_A	(							self				):







											"""simple docstring"""



											torch.manual_seed(0				)
											a__	:							str      						= 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") ,					)
											return model




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= self.dummy_uncond_unet
											a__	:							Optional[Any]      						= PNDMScheduler()

											a__	:							Union[str, Any]      						= PNDMPipeline(unet=__UpperCAmelCase ,					scheduler=__UpperCAmelCase				)
											pndm.to(__UpperCAmelCase				)
											pndm.set_progress_bar_config(disable=__UpperCAmelCase				)

											a__	:							Tuple      						= torch.manual_seed(0				)
											a__	:							Tuple      						= pndm(generator=__UpperCAmelCase ,					num_inference_steps=20 ,					output_type="numpy"				).images

											a__	:							Optional[int]      						= torch.manual_seed(0				)
											a__	:							Tuple      						= pndm(generator=__UpperCAmelCase ,					num_inference_steps=20 ,					output_type="numpy" ,					return_dict=__UpperCAmelCase				)[0]

											a__	:							str      						= image[0, -3:, -3:, -1]
											a__	:							int      						= image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											a__	:							int      						= np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]				)

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





@slow
@require_torch
class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= "google/ddpm-cifar10-32"

											a__	:							List[Any]      						= UNetaDModel.from_pretrained(__UpperCAmelCase				)
											a__	:							Optional[Any]      						= PNDMScheduler()

											a__	:							int      						= PNDMPipeline(unet=__UpperCAmelCase ,					scheduler=__UpperCAmelCase				)
											pndm.to(__UpperCAmelCase				)
											pndm.set_progress_bar_config(disable=__UpperCAmelCase				)
											a__	:							Union[str, Any]      						= torch.manual_seed(0				)
											a__	:							Union[str, Any]      						= pndm(generator=__UpperCAmelCase ,					output_type="numpy"				).images

											a__	:							List[Any]      						= image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											a__	:							Dict      						= np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]				)

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


