






from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
a		:       Union[str, Any]			     = 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.
a		:       Any			     = 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.
a		:       Union[str, Any]			     = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))






def 				lowerCamelCase__      (    __lowerCamelCase			:     str  ,		__lowerCamelCase			:     str		):
  __UpperCAmelCase							:	Optional[Any]           =  len([g for position, g in enumerate(__lowerCamelCase		) if g == main_target[position]]		)
  return (item, float(__lowerCamelCase		))






def 				lowerCamelCase__      (    __lowerCamelCase			:     str  ,		__lowerCamelCase			:     str		):
  __UpperCAmelCase							:	Optional[int]           =  random.randint(0  ,		len(__lowerCamelCase		) - 1		)
  __UpperCAmelCase							:	Any           =  parent_a[:random_slice] + parent_a[random_slice:]
  __UpperCAmelCase							:	Union[str, Any]           =  parent_a[:random_slice] + parent_a[random_slice:]
  return (child_a, child_a)






def 				lowerCamelCase__      (    __lowerCamelCase			:     str  ,		__lowerCamelCase			:     list[str]		):
  __UpperCAmelCase							:	Tuple           =  list(__lowerCamelCase		)
  if random.uniform(0  ,		1		) < MUTATION_PROBABILITY:
    __UpperCAmelCase							:	Tuple           =  random.choice(__lowerCamelCase		)
  return "".join(__lowerCamelCase		)






def 				lowerCamelCase__      (    __lowerCamelCase			:     tuple[str, float]  ,		__lowerCamelCase			:     list[tuple[str, float]]  ,		__lowerCamelCase			:     list[str]  ,		):
  __UpperCAmelCase							:	Optional[Any]           =  []
  # Generate more children proportionally to the fitness score.
  __UpperCAmelCase							:	str           =  int(parent_a[1] * 100		) + 1
  __UpperCAmelCase							:	int           =  10 if child_n >= 10 else child_n
  for _ in range(__lowerCamelCase		):
    __UpperCAmelCase							:	Tuple           =  population_score[random.randint(0  ,		__lowerCamelCase		)][0]

    __UpperCAmelCase      , __UpperCAmelCase							:	List[Any]           =  crossover(parent_a[0]  ,		__lowerCamelCase		)
    # Append new string to the population list.
    pop.append(mutate(__lowerCamelCase  ,		__lowerCamelCase		)		)
    pop.append(mutate(__lowerCamelCase  ,		__lowerCamelCase		)		)
  return pop






def 				lowerCamelCase__      (    __lowerCamelCase			:     str  ,		__lowerCamelCase			:     list[str]  ,		__lowerCamelCase			:     bool = True		):

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

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

  # Just some logs to know what the algorithms is doing.
  __UpperCAmelCase      , __UpperCAmelCase							:	Optional[Any]           =  0, 0

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

    # 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.
    __UpperCAmelCase							:	Tuple           =  [evaluate(__lowerCamelCase  ,		__lowerCamelCase		) for item in population]

    # Check if there is a matching evolution.
    __UpperCAmelCase							:	List[str]           =  sorted(__lowerCamelCase  ,		key=lambda __lowerCamelCase		: x[1]  ,		reverse=__lowerCamelCase		)
    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 % 10 == 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.
    __UpperCAmelCase							:	Tuple           =  population[: int(N_POPULATION / 3		)]
    population.clear()
    population.extend(__lowerCamelCase		)
    # Normalize population score to be between 0 and 1.
    __UpperCAmelCase							:	Union[str, Any]           =  [
        (item, score / len(__lowerCamelCase		)) for item, score in population_score
    ]

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


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










from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

a		:       Dict			     = typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
a		:       Any			     = typing.Union[np.floataa, int, float]  # noqa: UP007






def 				lowerCamelCase__      (    __lowerCamelCase			:     Vector  ,		__lowerCamelCase			:     Vector		):
  return np.sqrt(np.sum((np.asarray(__lowerCamelCase		) - np.asarray(__lowerCamelCase		)) ** 2		)		)






def 				lowerCamelCase__      (    __lowerCamelCase			:     Vector  ,		__lowerCamelCase			:     Vector		):
  return sum((va - va) ** 2 for va, va in zip(__lowerCamelCase  ,		__lowerCamelCase		)		) ** (1 / 2)


if __name__ == "__main__":






       def 				lowerCamelCase__      (    ):
         from timeit import timeit

         print("""Without Numpy"""		)
         print(
             timeit(
                 """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""  ,		number=10000  ,		globals=globals()  ,		)		)
         print("""With Numpy"""		)
         print(
             timeit(
                 """euclidean_distance([1, 2, 3], [4, 5, 6])"""  ,		number=10000  ,		globals=globals()  ,		)		)







       benchmark()


