






'''simple docstring'''





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__lowerCamelCase	:		Tuple										=   2_00
# 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.
__lowerCamelCase	:		Dict										=   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.
__lowerCamelCase	:		Optional[int]										=   0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))







def 			A__							(      _a  :					Optional[int]		,  _a  :					Optional[int]	):





 '''simple docstring'''







 snake_case__  :    Optional[int]							       =len([g for position, g in enumerate(_A	) if g == main_target[position]]	)
 return (item, float(_A	))







def 			A__							(      _a  :					int		,  _a  :					str	):





 '''simple docstring'''







 snake_case__  :    Any							       =random.randint(0		,  len(_A	) - 1	)
 snake_case__  :    Tuple							       =parent_a[:random_slice] + parent_a[random_slice:]
 snake_case__  :    str							       =parent_a[:random_slice] + parent_a[random_slice:]
 return (child_a, child_a)







def 			A__							(      _a  :					int		,  _a  :					Union[str, Any]	):





 '''simple docstring'''







 snake_case__  :    List[Any]							       =list(_A	)
 if random.uniform(0		,  1	) < MUTATION_PROBABILITY:
  snake_case__  :    Optional[Any]							       =random.choice(_A	)
 return "".join(_A	)







def 			A__							(      _a  :					int		,  _a  :					Dict		,  _a  :					Any		,  ):





 '''simple docstring'''







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

  snake_case__       ,   snake_case__  :    Optional[int]							       =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 			A__							(      _a  :					Any		,  _a  :					Optional[int]		,  _a  :					Tuple = True	):





 '''simple docstring'''







 if N_POPULATION < N_SELECTED:
  snake_case__  :    List[str]							       =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.
 snake_case__  :    List[str]							       =sorted({c for c in target if c not in genes}	)
 if not_in_genes_list:
  snake_case__  :    Union[str, Any]							       =f"{not_in_genes_list} is not in genes list, evolution cannot converge"
  raise ValueError(_A	)

 # Generate random starting population.
 snake_case__  :    Tuple							       =[]
 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.
 snake_case__       ,   snake_case__  :    Any							       =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.
  snake_case__  :    List[str]							       =[evaluate(_A		,  _A	) for item in population]

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













from collections import namedtuple

import requests
from lxml import html  # type: ignore

__lowerCamelCase	:		Any										=   namedtuple("""covid_data""", """cases deaths recovered""")







def 			A__							(      _a  :					str = "https://www.worldometers.info/coronavirus/"	):





 '''simple docstring'''







 snake_case__  :    Any							       ="""//div[@class = \"maincounter-number\"]/span/text()"""
 return covid_data(*html.fromstring(requests.get(_a	).content	).xpath(_a	)	)


__lowerCamelCase	:		str										=   """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""

print(fmt.format(*covid_stats()))





