



'''simple docstring'''





def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase = 4000000      ):



    """simple docstring"""





    lowerCAmelCase__			:  str											= [0, 1]
    lowerCAmelCase__			:  Union[str, Any]											= 0
    while fib[i] <= n:
        fib.append(fib[i] + fib[i + 1]      )
        if fib[i + 2] > n:
            break
        i += 1
    lowerCAmelCase__			:  Optional[int]											= 0
    for j in range(len(UpperCamelCase      ) - 1      ):
        if fib[j] % 2 == 0:
            total += fib[j]

    return total


if __name__ == "__main__":
 print(F"""{solution() = }""")







'''simple docstring'''





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 = 100							,   UpperCamelCase = 0.01							,   UpperCamelCase = 1							,   ):



    """simple docstring"""





    lowerCAmelCase__			:  int											= False
    lowerCAmelCase__			:  Optional[Any]											= search_prob
    lowerCAmelCase__			:  Tuple											= start_temperate
    lowerCAmelCase__			:  Optional[Any]											= []
    lowerCAmelCase__			:  str											= 0
    lowerCAmelCase__			:  Tuple											= None

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



     """simple docstring"""





     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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



     """simple docstring"""





     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()}"""
 )


