






'''simple docstring'''






import math
import random
from typing import Any

from .hill_climbing import SearchProblem




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[int]  ,		A  :       bool = True  ,		A  :       float = math.inf  ,		A  :       float = -math.inf  ,		A  :       float = math.inf  ,		A  :       float = -math.inf  ,		A  :       bool = False  ,		A  :       float = 1_00  ,		A  :       float = 0.01  ,		A  :       float = 1  ,		)    -> Any:







			"""simple docstring"""
			__snake_case				:      List[str]     							= False
			__snake_case				:      List[str]     							= search_prob
			__snake_case				:      Any     							= start_temperate
			__snake_case				:      Any     							= []
			__snake_case				:      Union[str, Any]     							= 0
			__snake_case				:      Optional[int]     							= None

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

			if visualization:
						from matplotlib import pyplot as plt

						plt.plot(range(A  )  ,		A  )
						plt.xlabel('Iterations'  )
						plt.ylabel('Function values'  )
						plt.show()
			return best_state


if __name__ == "__main__":




						def        _SCREAMING_SNAKE_CASE  (				A  :       Dict  ,		A  :       List[str]  )    -> Dict:







									"""simple docstring"""
									return (x**2) + (y**2)

						# starting the problem with initial coordinates (12, 47)
						__A    	=   SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
						__A    	=   simulated_annealing(
						    prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, 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)
						__A    	=   SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
						__A    	=   simulated_annealing(
						    prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, 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  (				A  :       str  ,		A  :       List[Any]  )    -> Any:







									"""simple docstring"""
									return (3 * x**2) - (6 * y)

						__A    	=   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
						__A    	=   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()}'''
						)

						__A    	=   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
						__A    	=   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'''






__A    	=   {str(digit): digit**5 for digit in range(1_0)}




def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> int:







   """simple docstring"""
   return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A  )  )




def        _SCREAMING_SNAKE_CASE  (				)    -> int:







   """simple docstring"""
   return sum(
       number
       for number in range(10_00  ,		1_00_00_00  )
       if number == digits_fifth_powers_sum(A  )  )


if __name__ == "__main__":
      print(solution())