"""simple docstring"""



from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
SCREAMING_SNAKE_CASE__:Dict			=	200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
SCREAMING_SNAKE_CASE__:Tuple			=	50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
SCREAMING_SNAKE_CASE__:Optional[int]			=	0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))



def 					_lowerCamelCase(					a ,  a							):
       __a     				=  len([g for position, g in enumerate(a							) if g == main_target[position]]							)
       return (item, float(a							))



def 					_lowerCamelCase(					a ,  a							):
       __a     				=  random.randint(0 ,  len(a							) - 1							)
       __a     				=  parent_a[:random_slice] + parent_a[random_slice:]
       __a     				=  parent_a[:random_slice] + parent_a[random_slice:]
       return (child_a, child_a)



def 					_lowerCamelCase(					a ,  a							):
       __a     				=  list(a							)
       if random.uniform(0 ,  1							) < MUTATION_PROBABILITY:
              __a     				=  random.choice(a							)
       return "".join(a							)



def 					_lowerCamelCase(					a ,  a ,  a ,  ):
       __a     				=  []
       # Generate more children proportionally to the fitness score.
       __a     				=  int(parent_a[1] * 1_0_0							) + 1
       __a     				=  1_0 if child_n >= 1_0 else child_n
       for _ in range(a							):
              __a     				=  population_score[random.randint(0 ,  a							)][0]

              __a				, __a     				=  crossover(parent_a[0] ,  a							)
              # Append new string to the population list.
              pop.append(mutate(a ,  a							)							)
              pop.append(mutate(a ,  a							)							)
       return pop



def 					_lowerCamelCase(					a ,  a ,  a = True							):

       # Verify if N_POPULATION is bigger than N_SELECTED
       if N_POPULATION < N_SELECTED:
              __a     				=  F"{N_POPULATION} must be bigger than {N_SELECTED}"
              raise ValueError(a							)
       # Verify that the target contains no genes besides the ones inside genes variable.
       __a     				=  sorted({c for c in target if c not in genes}							)
       if not_in_genes_list:
              __a     				=  F"{not_in_genes_list} is not in genes list, evolution cannot converge"
              raise ValueError(a							)

       # Generate random starting population.
       __a     				=  []
       for _ in range(a							):
              population.append("".join([random.choice(a							) for i in range(len(a							)							)]							)							)

       # Just some logs to know what the algorithms is doing.
       __a				, __a     				=  0, 0

       # This loop will end when we find a perfect match for our target.
       while True:
              generation += 1
              total_population += len(a							)

              # Random population created. Now it's time to evaluate.

              # Adding a bit of concurrency can make everything faster,
              #
              # import concurrent.futures
              # population_score: list[tuple[str, float]] = []
              # with concurrent.futures.ThreadPoolExecutor(
              #                                   max_workers=NUM_WORKERS) as executor:
              #     futures = {executor.submit(evaluate, item) for item in population}
              #     concurrent.futures.wait(futures)
              #     population_score = [item.result() for item in futures]
              #
              # but with a simple algorithm like this, it will probably be slower.
              # We just need to call evaluate for every item inside the population.
              __a     				=  [evaluate(a ,  a							) for item in population]

              # Check if there is a matching evolution.
              __a     				=  sorted(a ,  key=lambda a							: x[1] ,  reverse=a							)
              if population_score[0][0] == target:
                     return (generation, total_population, population_score[0][0])

              # Print the best result every 10 generation.
              # Just to know that the algorithm is working.
              if debug and generation % 1_0 == 0:
                     print(
                         F"\nGeneration: {generation}"
                         F"\nTotal Population:{total_population}"
                         F"\nBest score: {population_score[0][1]}"
                         F"\nBest string: {population_score[0][0]}"							)

              # Flush the old population, keeping some of the best evolutions.
              # Keeping this avoid regression of evolution.
              __a     				=  population[: int(N_POPULATION / 3							)]
              population.clear()
              population.extend(a							)
              # Normalize population score to be between 0 and 1.
              __a     				=  [
                  (item, score / len(a							)) for item, score in population_score
              ]

              # This is selection
              for i in range(a							):
                     population.extend(select(population_score[int(a							)] ,  a ,  a							)							)
                     # Check if the population has already reached the maximum value and if so,
                     # break the cycle.  If this check is disabled, the algorithm will take
                     # forever to compute large strings, but will also calculate small strings in
                     # a far fewer generations.
                     if len(a							) > N_POPULATION:
                            break


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__:Union[str, Any]			=	(
     """This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
 )
 SCREAMING_SNAKE_CASE__:Any			=	list(
     """ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
     """nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"""
 )
 SCREAMING_SNAKE_CASE__:str			=	basic(target_str, genes_list)
 print(
     F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
 )

"""simple docstring"""



from __future__ import annotations



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a ,  ):
       __a     				=  len(a							)

       # If row is equal to the size of the board it means there are a queen in each row in
       # the current board (possible_board)
       if row == n:
              # We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
              # this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
              boards.append([". " * i + "Q " + ". " * (n - 1 - i) for i in possible_board]							)
              return

       # We iterate each column in the row to find all possible results in each row
       for col in range(a							):
              # We apply that we learned previously. First we check that in the current board
              # (possible_board) there are not other same value because if there is it means
              # that there are a collision in vertical. Then we apply the two formulas we
              # learned before:
              #
              # 45º: y - x = b or 45: row - col = b
              # 135º: y + x = b or row + col = b.
              #
              # And we verify if the results of this two formulas not exist in their variables
              # respectively.  (diagonal_right_collisions, diagonal_left_collisions)
              #
              # If any or these are True it means there is a collision so we continue to the
              # next value in the for loop.
              if (
                  col in possible_board
                  or row - col in diagonal_right_collisions
                  or row + col in diagonal_left_collisions
              ):
                     continue

              # If it is False we call dfs function again and we update the inputs
              depth_first_search(
                  [*possible_board, col] ,  [*diagonal_right_collisions, row - col] ,  [*diagonal_left_collisions, row + col] ,  a ,  a ,  )



def 					_lowerCamelCase(					a							):
       __a     				=  []
       depth_first_search([] ,  [] ,  [] ,  a ,  a							)

       # Print all the boards
       for board in boards:
              for column in board:
                     print(a							)
              print(""							)

       print(len(a							) ,  "solutions were found."							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   n_queens_solution(4)
