


import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def __lowercase     (      lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      int = True						,					lowerCamelCase_  :      List[Any] = math.inf						,					lowerCamelCase_  :      Any = -math.inf						,					lowerCamelCase_  :      str = math.inf						,					lowerCamelCase_  :      Union[str, Any] = -math.inf						,					lowerCamelCase_  :      Union[str, Any] = False						,					lowerCamelCase_  :      Optional[Any] = 100						,					lowerCamelCase_  :      str = 0.01						,					lowerCamelCase_  :      Dict = 1						,					):
				SCREAMING_SNAKE_CASE__     							=				False
				SCREAMING_SNAKE_CASE__     							=				search_prob
				SCREAMING_SNAKE_CASE__     							=				start_temperate
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				0
				SCREAMING_SNAKE_CASE__     							=				None

				while not search_end:
								SCREAMING_SNAKE_CASE__     							=				current_state.score()
								if best_state is None or current_score > best_state.score():
												SCREAMING_SNAKE_CASE__     							=				current_state
								scores.append(lowerCamelCase_  )
								iterations += 1
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				current_state.get_neighbors()
								while (
								    next_state is None and neighbors
								):  # till we do not find a neighbor that we can move to
												SCREAMING_SNAKE_CASE__     							=				random.randint(0						,					len(lowerCamelCase_  ) - 1  )  # picking a random neighbor
												SCREAMING_SNAKE_CASE__     							=				neighbors.pop(lowerCamelCase_  )
												SCREAMING_SNAKE_CASE__     							=				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:
																SCREAMING_SNAKE_CASE__     							=				change * -1  # in case we are finding minimum
												if change > 0:  # improves the solution
																SCREAMING_SNAKE_CASE__     							=				picked_neighbor
												else:
																SCREAMING_SNAKE_CASE__     							=				(math.e) ** (
																    change / current_temp
																)  # probability generation function
																if random.random() < probability:  # random number within probability
																				SCREAMING_SNAKE_CASE__     							=				picked_neighbor
								SCREAMING_SNAKE_CASE__     							=				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
												SCREAMING_SNAKE_CASE__     							=				True
								else:
												SCREAMING_SNAKE_CASE__     							=				next_state

				if visualization:
								from matplotlib import pyplot as plt

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


if __name__ == "__main__":


	def __lowercase     (      lowerCamelCase_  :      Any						,					lowerCamelCase_  :      List[Any]  ):
					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=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()}"""
	)

	# 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=100, 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 __lowercase     (      lowerCamelCase_  :      List[Any]						,					lowerCamelCase_  :      List[str]  ):
					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()}"""
	)




"""simple docstring"""






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin


class 			lowerCamelCase_		(			lowercase       ,  lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					1



				@register_to_config
				def __init__(						self       ,		UpperCAmelCase__=2000       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=20       ,		UpperCAmelCase__=1e-3     ):
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				None



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None     ):
								SCREAMING_SNAKE_CASE__     							=				torch.linspace(1       ,		self.config.sampling_eps       ,		UpperCAmelCase__       ,		device=UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__=None     ):
								if self.timesteps is None:
												raise ValueError(
												    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"     )

								# TODO(Patrick) better comments + non-PyTorch
								# postprocess model score
								SCREAMING_SNAKE_CASE__     							=				(
								    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
								)
								SCREAMING_SNAKE_CASE__     							=				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff     )     )
								SCREAMING_SNAKE_CASE__     							=				std.flatten()
								while len(std.shape     ) < len(score.shape     ):
												SCREAMING_SNAKE_CASE__     							=				std.unsqueeze(-1     )
								SCREAMING_SNAKE_CASE__     							=				-score / std

								# compute
								SCREAMING_SNAKE_CASE__     							=				-1.0 / len(self.timesteps     )

								SCREAMING_SNAKE_CASE__     							=				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
								SCREAMING_SNAKE_CASE__     							=				beta_t.flatten()
								while len(beta_t.shape     ) < len(x.shape     ):
												SCREAMING_SNAKE_CASE__     							=				beta_t.unsqueeze(-1     )
								SCREAMING_SNAKE_CASE__     							=				-0.5 * beta_t * x

								SCREAMING_SNAKE_CASE__     							=				torch.sqrt(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				drift - diffusion**2 * score
								SCREAMING_SNAKE_CASE__     							=				x + drift * dt

								# add noise
								SCREAMING_SNAKE_CASE__     							=				randn_tensor(x.shape       ,		layout=x.layout       ,		generator=UpperCAmelCase__       ,		device=x.device       ,		dtype=x.dtype     )
								SCREAMING_SNAKE_CASE__     							=				x_mean + diffusion * math.sqrt(-dt     ) * noise

								return x, x_mean






				def __len__(						self     ):
								return self.config.num_train_timesteps
