
"""simple docstring"""





from __future__ import annotations

import random

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







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					int			     =    len([g for position, g in enumerate(__lowerCamelCase  ) if g == main_target[position]]  )
   return (item, float(__lowerCamelCase  ))







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Tuple			     =    random.randint(0,		len(__lowerCamelCase  ) - 1  )
   UpperCAmelCase_					:					Optional[Any]			     =    parent_a[:random_slice] + parent_a[random_slice:]
   UpperCAmelCase_					:					Optional[int]			     =    parent_a[:random_slice] + parent_a[random_slice:]
   return (child_a, child_a)







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Optional[Any]			     =    list(__lowerCamelCase  )
   if random.uniform(0,		1  ) < MUTATION_PROBABILITY:
      UpperCAmelCase_					:					List[str]			     =    random.choice(__lowerCamelCase  )
   return "".join(__lowerCamelCase  )







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

      UpperCAmelCase_     ,   UpperCAmelCase_					:					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 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = True  ):

   # Verify if N_POPULATION is bigger than N_SELECTED
   if N_POPULATION < N_SELECTED:
      UpperCAmelCase_					:					List[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.
   UpperCAmelCase_					:					List[str]			     =    sorted({c for c in target if c not in genes}  )
   if not_in_genes_list:
      UpperCAmelCase_					:					List[Any]			     =    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_					:					List[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_					:					Optional[Any]			     =    [evaluate(__lowerCamelCase,		__lowerCamelCase  ) for item in population]

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




"""simple docstring"""





from __future__ import annotations







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   if (voltage, current, resistance).count(0  ) != 1:
      raise ValueError("One and only one argument must be 0"  )
   if resistance < 0:
      raise ValueError("Resistance cannot be negative"  )
   if voltage == 0:
      return {"voltage": float(current * resistance  )}
   elif current == 0:
      return {"current": voltage / resistance}
   elif resistance == 0:
      return {"resistance": voltage / current}
   else:
      raise ValueError("Exactly one argument must be 0"  )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


