'''simple docstring'''





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCAmelCase_	:		Tuple			=     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.
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, 1000))
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			tuple[str, float]:




      '''simple docstring'''
      _UpperCamelCase			:			Optional[int]   			=						len([g for position, g in enumerate(_A					) if g == main_target[position]]					)
      return (item, float(_A					))
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			tuple[str, str]:




      '''simple docstring'''
      _UpperCamelCase			:			Tuple   			=						random.randint(0     ,len(_A					) - 1					)
      _UpperCamelCase			:			Tuple   			=						parent_a[:random_slice] + parent_a[random_slice:]
      _UpperCamelCase			:			str   			=						parent_a[:random_slice] + parent_a[random_slice:]
      return (child_a, child_a)
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			str:




      '''simple docstring'''
      _UpperCamelCase			:			Optional[int]   			=						list(_A					)
      if random.uniform(0     ,1					) < MUTATION_PROBABILITY:
            _UpperCamelCase			:			List[Any]   			=						random.choice(_A					)
      return "".join(_A					)
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase     ,)     ->			list[str]:




      '''simple docstring'''
      _UpperCamelCase			:			int   			=						[]
      # Generate more children proportionally to the fitness score.
      _UpperCamelCase			:			Dict   			=						int(parent_a[1] * 1_0_0					) + 1
      _UpperCamelCase			:			Dict   			=						1_0 if child_n >= 1_0 else child_n
      for _ in range(_A					):
            _UpperCamelCase			:			Tuple   			=						population_score[random.randint(0     ,_A					)][0]

            _UpperCamelCase			,       _UpperCamelCase			:			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							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase = True					)     ->			tuple[int, int, str]:




      '''simple docstring'''

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

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

            # Check if there is a matching evolution.
            _UpperCamelCase			:			Optional[int]   			=						sorted(_A     ,key=lambda UpperCAmelCase					: 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 % 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.
            _UpperCamelCase			:			Any   			=						population[: int(N_POPULATION / 3					)]
            population.clear()
            population.extend(_A					)
            # Normalize population score to be between 0 and 1.
            _UpperCamelCase			:			str   			=						[
                (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}"""
   )



'''simple docstring'''





import random




class 		SCREAMING_SNAKE_CASE     :







				'''simple docstring'''





				@staticmethod
				def  snake_case__				(						lowercase__					:						str					)							->tuple[list[int], list[int]]:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						[ord(lowercase__					) for i in text]
										_UpperCamelCase			:			Dict   			=						[]
										_UpperCamelCase			:			Optional[int]   			=						[]
										for i in plain:
																_UpperCamelCase			:			Tuple   			=						random.randint(1       ,		300					)
																_UpperCamelCase			:			Any   			=						(i + k) * k
																cipher.append(lowercase__					)
																key.append(lowercase__					)
										return cipher, key







				@staticmethod
				def  snake_case__				(						lowercase__					:						list[int]       ,		lowercase__					:						list[int]					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						[]
										for i in range(len(lowercase__					)					):
																_UpperCamelCase			:			List[Any]   			=						int((cipher[i] - (key[i]) ** 2) / key[i]					)
																plain.append(chr(lowercase__					)					)
										return "".join(lowercase__					)


if __name__ == "__main__":
			lowerCAmelCase_	,	lowerCAmelCase_	:		Optional[int]			=     Onepad().encrypt("""Hello""")
			print(c, k)
			print(Onepad().decrypt(c, k))


