'''simple docstring'''




from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat






def __lowerCAmelCase (							)	->    tuple[list[int], int]:
    __lowerCamelCase		=  [randint(-10_00				,  10_00				) for i in range(10				)]
    __lowerCamelCase		=  randint(-50_00				,  50_00				)
    return (arr, r)


__UpperCAmelCase     							=make_dataset()






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    tuple[int, ...]:
    for triplet in permutations(UpperCamelCase__				,  3				):
        if sum(UpperCamelCase__				) == target:
            return tuple(sorted(UpperCamelCase__				)				)
    return (0, 0, 0)






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    tuple[int, int, int]:
    arr.sort()
    __lowerCamelCase		=  len(UpperCamelCase__				)
    for i in range(n - 1				):
        __lowerCamelCase	,  __lowerCamelCase		=  i + 1, n - 1
        while left < right:
            if arr[i] + arr[left] + arr[right] == target:
                return (arr[i], arr[left], arr[right])
            elif arr[i] + arr[left] + arr[right] < target:
                left += 1
            elif arr[i] + arr[left] + arr[right] > target:
                right -= 1
    return (0, 0, 0)






def __lowerCAmelCase (							)	->    tuple[float, float]:
    __lowerCamelCase		=  '''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
    __lowerCamelCase		=  '''
triplet_sum1(*dataset)
'''
    __lowerCamelCase		=  '''
triplet_sum2(*dataset)
'''
    __lowerCamelCase		=  repeat(setup=UpperCamelCase__				,  stmt=UpperCamelCase__				,  repeat=5				,  number=1_00_00				)
    __lowerCamelCase		=  repeat(setup=UpperCamelCase__				,  stmt=UpperCamelCase__				,  repeat=5				,  number=1_00_00				)
    return (min(UpperCamelCase__				), min(UpperCamelCase__				))


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  __UpperCAmelCase     							=solution_times()
  print(f'The time for naive implementation is {times[0]}.')
  print(f'The time for optimized implementation is {times[1]}.')



from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_UpperCamelCase 			=		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.
_UpperCamelCase 			=		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.
_UpperCamelCase 			=		0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						List[str]					=					len([g for position, g in enumerate(lowercase__						) if g == main_target[position]]						)
      return (item, float(lowercase__						))







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						str					=					random.randint(0   ,	len(lowercase__						) - 1						)
      __lowerCAmelCase					:						int					=					parent_a[:random_slice] + parent_a[random_slice:]
      __lowerCAmelCase					:						Dict					=					parent_a[:random_slice] + parent_a[random_slice:]
      return (child_a, child_a)







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						List[str]					=					list(lowercase__						)
      if random.uniform(0   ,	1						) < MUTATION_PROBABILITY:
            __lowerCAmelCase					:						int					=					random.choice(lowercase__						)
      return "".join(lowercase__						)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	):
      __lowerCAmelCase					:						str					=					[]
      # Generate more children proportionally to the fitness score.
      __lowerCAmelCase					:						str					=					int(parent_a[1] * 1_0_0						) + 1
      __lowerCAmelCase					:						Optional[Any]					=					1_0 if child_n >= 1_0 else child_n
      for _ in range(lowercase__						):
            __lowerCAmelCase					:						List[Any]					=					population_score[random.randint(0   ,	lowercase__						)][0]

            __lowerCAmelCase,							__lowerCAmelCase					:						Dict					=					crossover(parent_a[0]   ,	lowercase__						)
            # Append new string to the population list.
            pop.append(mutate(lowercase__   ,	lowercase__						)						)
            pop.append(mutate(lowercase__   ,	lowercase__						)						)
      return pop







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__ = True						):

      # Verify if N_POPULATION is bigger than N_SELECTED
      if N_POPULATION < N_SELECTED:
            __lowerCAmelCase					:						int					=					f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
            raise ValueError(lowercase__						)
      # Verify that the target contains no genes besides the ones inside genes variable.
      __lowerCAmelCase					:						Any					=					sorted({c for c in target if c not in genes}						)
      if not_in_genes_list:
            __lowerCAmelCase					:						List[str]					=					f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
            raise ValueError(lowercase__						)

      # Generate random starting population.
      __lowerCAmelCase					:						List[Any]					=					[]
      for _ in range(lowercase__						):
            population.append(''''''.join([random.choice(lowercase__						) for i in range(len(lowercase__						)						)]						)						)

      # Just some logs to know what the algorithms is doing.
      __lowerCAmelCase,							__lowerCAmelCase					:						Tuple					=					0, 0

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

            # 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.
            __lowerCAmelCase					:						Any					=					[evaluate(lowercase__   ,	lowercase__						) for item in population]

            # Check if there is a matching evolution.
            __lowerCAmelCase					:						Union[str, Any]					=					sorted(lowercase__   ,	key=lambda lowercase__						: x[1]   ,	reverse=lowercase__						)
            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.
            __lowerCAmelCase					:						Tuple					=					population[: int(N_POPULATION / 3						)]
            population.clear()
            population.extend(lowercase__						)
            # Normalize population score to be between 0 and 1.
            __lowerCAmelCase					:						List[Any]					=					[
                (item, score / len(lowercase__						)) for item, score in population_score
            ]

            # This is selection
            for i in range(lowercase__						):
                  population.extend(select(population_score[int(lowercase__						)]   ,	lowercase__   ,	lowercase__						)						)
                  # 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(lowercase__						) > N_POPULATION:
                        break


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


