


'''simple docstring'''


from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__magic_name__			=      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.
__magic_name__			=      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.
__magic_name__			=      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):
      A_						:  List[str]          =				len([g for position, g in enumerate(lowerCamelCase) if g == main_target[position]])
      return (item, float(lowerCamelCase))




def      lowerCamelCase					(      lowerCamelCase  :      str							,						lowerCamelCase  :      str):
      A_						:  Dict          =				random.randint(0							,						len(lowerCamelCase) - 1)
      A_						:  List[Any]          =				parent_a[:random_slice] + parent_a[random_slice:]
      A_						:  List[str]          =				parent_a[:random_slice] + parent_a[random_slice:]
      return (child_a, child_a)




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




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

            A_						:  str          =				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:
            A_						:  str          =				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.
      A_						:  List[Any]          =				sorted({c for c in target if c not in genes})
      if not_in_genes_list:
            A_						:  Any          =				F'{not_in_genes_list} is not in genes list, evolution cannot converge'
            raise ValueError(lowerCamelCase)

      # Generate random starting population.
      A_						:  Tuple          =				[]
      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.
      A_						:  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.
            A_						:  Optional[Any]          =				[evaluate(lowerCamelCase							,						lowerCamelCase) for item in population]

            # Check if there is a matching evolution.
            A_						:  int          =				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.
            A_						:  int          =				population[: int(N_POPULATION / 3)]
            population.clear()
            population.extend(lowerCamelCase)
            # Normalize population score to be between 0 and 1.
            A_						:  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__":
 __magic_name__			=      (
     "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
 )
 __magic_name__			=      list(
     ' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'
     'nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'
 )
 __magic_name__			=      basic(target_str, genes_list)
 print(
     f"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
 )




'''simple docstring'''


import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
     print('Googling.....')
     __magic_name__			=      'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
     __magic_name__			=      requests.get(url, headers={'UserAgent': UserAgent().random})
     # res.raise_for_status()
     with open('project1a.html', 'wb') as out_file:  # only for knowing the class
          for data in res.iter_content(10_000):
               out_file.write(data)
     __magic_name__			=      BeautifulSoup(res.text, 'html.parser')
     __magic_name__			=      list(soup.select('.eZt8xd'))[:5]

     print(len(links))
     for link in links:
          if link.text == "Maps":
               webbrowser.open(link.get('href'))
          else:
               webbrowser.open(f"""https://google.com{link.get('href')}""")
