"""simple docstring"""




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      :		List[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      :		List[Any]           =   0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       str	)       ->      tuple[str, float]:
  _lowerCAmelCase						:       List[Any]  			=     len([g for position, g in enumerate(_lowerCamelCase	) if g == main_target[position]]	)
  return (item, float(_lowerCamelCase	))





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       str	)       ->      tuple[str, str]:
  _lowerCAmelCase						:       Dict  			=     random.randint(0			,len(_lowerCamelCase	) - 1	)
  _lowerCAmelCase						:       Tuple  			=     parent_a[:random_slice] + parent_a[random_slice:]
  _lowerCAmelCase						:       Optional[int]  			=     parent_a[:random_slice] + parent_a[random_slice:]
  return (child_a, child_a)





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       list[str]	)       ->      str:
  _lowerCAmelCase						:       Optional[Any]  			=     list(_lowerCamelCase	)
  if random.uniform(0			,1	) < MUTATION_PROBABILITY:
    _lowerCAmelCase						:       Optional[int]  			=     random.choice(_lowerCamelCase	)
  return "".join(_lowerCamelCase	)





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

    _lowerCAmelCase			,	_lowerCAmelCase						:       int  			=     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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       list[str]			,_lowerCamelCase						:       bool = True	)       ->      tuple[int, int, str]:

  # Verify if N_POPULATION is bigger than N_SELECTED
  if N_POPULATION < N_SELECTED:
    _lowerCAmelCase						:       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.
  _lowerCAmelCase						:       Dict  			=     sorted({c for c in target if c not in genes}	)
  if not_in_genes_list:
    _lowerCAmelCase						:       Optional[int]  			=     f"{not_in_genes_list} is not in genes list, evolution cannot converge"
    raise ValueError(_lowerCamelCase	)

  # Generate random starting population.
  _lowerCAmelCase						:       Optional[int]  			=     []
  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.
  _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(_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.
    _lowerCAmelCase						:       Dict  			=     [evaluate(_lowerCamelCase			,_lowerCamelCase	) for item in population]

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






import os




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					os.path.join(os.path.dirname(snake_case__					)						,   '''num.txt'''					)
				with open(snake_case__					) as file_hand:
								return str(sum(int(snake_case__					) for line in file_hand					)					)[:10]


if __name__ == "__main__":
						print(solution())




