



'''simple docstring'''

from __future__ import annotations

__lowerCAmelCase									=   8.988e9  # units = N * m^s * C^-2



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      dict[str, float]:
		_a     :      Tuple        =							abs(chargea * chargea  )

		if (force, chargea, chargea, distance).count(0  ) != 1:
				raise ValueError('One and only one argument must be 0'  )
		if distance < 0:
				raise ValueError('Distance cannot be negative'  )
		if force == 0:
				_a     :      Dict        =							COULOMBS_CONSTANT * charge_product / (distance**2)
				return {"force": force}
		elif chargea == 0:
				_a     :      Dict        =							abs(lowerCAmelCase_  ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
				return {"charge1": chargea}
		elif chargea == 0:
				_a     :      int        =							abs(lowerCAmelCase_  ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
				return {"charge2": chargea}
		elif distance == 0:
				_a     :      List[Any]        =							(COULOMBS_CONSTANT * charge_product / abs(lowerCAmelCase_  )) ** 0.5
				return {"distance": distance}
		raise ValueError('Exactly one argument must be 0'  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()







from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_UpperCamelCase 			=		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.
_UpperCamelCase 			=		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.
_UpperCamelCase 			=		0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						List[str]					=					len([g for position, g in enumerate(lowercase__						) if g == main_target[position]]						)
      return (item, float(lowercase__						))







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						str					=					random.randint(0   ,	len(lowercase__						) - 1						)
      __lowerCAmelCase					:						int					=					parent_a[:random_slice] + parent_a[random_slice:]
      __lowerCAmelCase					:						Dict					=					parent_a[:random_slice] + parent_a[random_slice:]
      return (child_a, child_a)







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







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	):
      __lowerCAmelCase					:						str					=					[]
      # Generate more children proportionally to the fitness score.
      __lowerCAmelCase					:						str					=					int(parent_a[1] * 1_0_0						) + 1
      __lowerCAmelCase					:						Optional[Any]					=					1_0 if child_n >= 1_0 else child_n
      for _ in range(lowercase__						):
            __lowerCAmelCase					:						List[Any]					=					population_score[random.randint(0   ,	lowercase__						)][0]

            __lowerCAmelCase,							__lowerCAmelCase					:						Dict					=					crossover(parent_a[0]   ,	lowercase__						)
            # Append new string to the population list.
            pop.append(mutate(lowercase__   ,	lowercase__						)						)
            pop.append(mutate(lowercase__   ,	lowercase__						)						)
      return pop







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__ = True						):

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

      # Generate random starting population.
      __lowerCAmelCase					:						List[Any]					=					[]
      for _ in range(lowercase__						):
            population.append(''''''.join([random.choice(lowercase__						) for i in range(len(lowercase__						)						)]						)						)

      # 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(lowercase__						)

            # 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					:						Any					=					[evaluate(lowercase__   ,	lowercase__						) for item in population]

            # Check if there is a matching evolution.
            __lowerCAmelCase					:						Union[str, Any]					=					sorted(lowercase__   ,	key=lambda lowercase__						: x[1]   ,	reverse=lowercase__						)
            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 % 1_0 == 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					:						Tuple					=					population[: int(N_POPULATION / 3						)]
            population.clear()
            population.extend(lowercase__						)
            # Normalize population score to be between 0 and 1.
            __lowerCAmelCase					:						List[Any]					=					[
                (item, score / len(lowercase__						)) for item, score in population_score
            ]

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


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


