



'''simple docstring'''






import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def       lowerCamelCase__       (						_A ,			_A = True ,			_A = math.inf ,			_A = -math.inf ,			_A = math.inf ,			_A = -math.inf ,			_A = False ,			_A = 100 ,			_A = 0.01 ,			_A = 1 ,			):
  a						:					Union[str, Any]        =     False
  a						:					List[Any]        =     search_prob
  a						:					Any        =     start_temperate
  a						:					List[str]        =     []
  a						:					Any        =     0
  a						:					str        =     None

  while not search_end:
    a						:					Union[str, Any]        =     current_state.score()
    if best_state is None or current_score > best_state.score():
      a						:					Tuple        =     current_state
    scores.append(A__				)
    iterations += 1
    a						:					Dict        =     None
    a						:					int        =     current_state.get_neighbors()
    while (
        next_state is None and neighbors
    ):  # till we do not find a neighbor that we can move to
      a						:					str        =     random.randint(0 ,			len(A__				) - 1				)  # picking a random neighbor
      a						:					Dict        =     neighbors.pop(A__				)
      a						:					str        =     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						:					List[str]        =     change * -1  # in case we are finding minimum
      if change > 0:  # improves the solution
        a						:					Optional[Any]        =     picked_neighbor
      else:
        a						:					Optional[Any]        =     (math.e) ** (
            change / current_temp
        )  # probability generation function
        if random.random() < probability:  # random number within probability
          a						:					Tuple        =     picked_neighbor
    a						:					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
      a						:					Optional[Any]        =     True
    else:
      a						:					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       lowerCamelCase__       (						_A ,			_A				):
       return (x**2) + (y**2)

     # starting the problem with initial coordinates (12, 47)
     lowerCAmelCase:					Tuple						       =						SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
     lowerCAmelCase:					Optional[Any]						       =						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)
     lowerCAmelCase:					Optional[Any]						       =						SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
     lowerCAmelCase:					List[Any]						       =						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       lowerCamelCase__       (						_A ,			_A				):
       return (3 * x**2) - (6 * y)

     lowerCAmelCase:					Optional[int]						       =						SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     lowerCAmelCase:					Tuple						       =						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:					List[Any]						       =						SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     lowerCAmelCase:					str						       =						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'''


def 				__lowerCamelCase				(   A__    )       ->	list:






   """simple docstring"""
   UpperCamelCase					=  len(A__    )
   for i in range(1    ,						A__    ):
      UpperCamelCase					=  collection[i]
      UpperCamelCase					=  0
      UpperCamelCase					=  i - 1

      while low <= high:
         UpperCamelCase					=  (low + high) // 2
         if val < collection[mid]:
            UpperCamelCase					=  mid - 1
         else:
            UpperCamelCase					=  mid + 1
      for j in range(A__    ,						A__    ,						-1    ):
         UpperCamelCase					=  collection[j - 1]
      UpperCamelCase					=  val
   return collection


if __name__ == "__main__":
   _lowerCamelCase	:       int      	=	input("Enter numbers separated by a comma:\n").strip()
   _lowerCamelCase	:       Union[str, Any]      	=	[int(item) for item in user_input.split(",")]
   print(binary_insertion_sort(unsorted))

