





"""simple docstring"""





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowercase__				:Any				   =       2_0_0
# 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.
lowercase__				:Any				   =       5_0
# 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.
lowercase__				:int				   =       0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))


def 					lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->tuple[str, float]:





  """simple docstring"""


  __UpperCAmelCase    :						Dict       =						len([g for position, g in enumerate(UpperCAmelCase_  ) if g == main_target[position]]  )
  return (item, float(UpperCAmelCase_  ))


def 					lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->tuple[str, str]:





  """simple docstring"""


  __UpperCAmelCase    :						int       =						random.randint(0  ,			len(UpperCAmelCase_  ) - 1  )
  __UpperCAmelCase    :						Tuple       =						parent_a[:random_slice] + parent_a[random_slice:]
  __UpperCAmelCase    :						Dict       =						parent_a[:random_slice] + parent_a[random_slice:]
  return (child_a, child_a)


def 					lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->str:





  """simple docstring"""


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


def 					lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			)						->list[str]:





  """simple docstring"""


  __UpperCAmelCase    :						Tuple       =						[]
  # Generate more children proportionally to the fitness score.
  __UpperCAmelCase    :						Optional[Any]       =						int(parent_a[1] * 1_00  ) + 1
  __UpperCAmelCase    :						Optional[Any]       =						10 if child_n >= 10 else child_n
  for _ in range(UpperCAmelCase_  ):
    __UpperCAmelCase    :						Optional[int]       =						population_score[random.randint(0  ,			UpperCAmelCase_  )][0]

    __UpperCAmelCase    :						Tuple       =						crossover(parent_a[0]  ,			UpperCAmelCase_  )
    # Append new string to the population list.
    pop.append(mutate(UpperCAmelCase_  ,			UpperCAmelCase_  )  )
    pop.append(mutate(UpperCAmelCase_  ,			UpperCAmelCase_  )  )
  return pop


def 					lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_ = True  )						->tuple[int, int, str]:





  """simple docstring"""


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

  # Generate random starting population.
  __UpperCAmelCase    :						Dict       =						[]
  for _ in range(UpperCAmelCase_  ):
    population.append(''''''.join([random.choice(UpperCAmelCase_  ) for i in range(len(UpperCAmelCase_  )  )]  )  )

  # Just some logs to know what the algorithms is doing.
  __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(UpperCAmelCase_  )

    # 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    :						str       =						[evaluate(UpperCAmelCase_  ,			UpperCAmelCase_  ) for item in population]

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

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


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






"""simple docstring"""





from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowercase__				:str				   =       (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase__				:list[int]				   =       [ord(letter) for letter in string.ascii_lowercase]
lowercase__				:set[int]				   =       {ord(char) for char in VALID_CHARS}

lowercase__				:list[str]				   =       ["the", "be", "to", "of", "and", "in", "that", "have"]


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->str | None:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						""
  __UpperCAmelCase    :						int
  __UpperCAmelCase    :						int
  __UpperCAmelCase    :						int

  for keychar, cipherchar in zip(cycle(UpperCAmelCase_  )  ,			UpperCAmelCase_  ):
    __UpperCAmelCase    :						Optional[Any]       =						cipherchar ^ keychar
    if decodedchar not in VALID_INTS:
      return None
    decoded += chr(UpperCAmelCase_  )

  return decoded


def       lowerCamelCase_       (   UpperCAmelCase_  )						->list[str]:





  """simple docstring"""


  __UpperCAmelCase    :						list[str]       =						[]
  for key in product(UpperCAmelCase_  ,			repeat=3  ):
    __UpperCAmelCase    :						List[Any]       =						try_key(UpperCAmelCase_  ,			UpperCAmelCase_  )
    if encoded is not None:
      possibles.append(UpperCAmelCase_  )
  return possibles


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->list[str]:





  """simple docstring"""


  return [possible for possible in possibles if common_word in possible.lower()]


def       lowerCamelCase_       (   UpperCAmelCase_ = "p059_cipher.txt"  )						->int:





  """simple docstring"""


  __UpperCAmelCase    :						list[int]
  __UpperCAmelCase    :						list[str]
  __UpperCAmelCase    :						str
  __UpperCAmelCase    :						str
  __UpperCAmelCase    :						str       =						Path(UpperCAmelCase_  ).parent.joinpath(UpperCAmelCase_  ).read_text(encoding='''utf-8'''  )

  __UpperCAmelCase    :						List[str]       =						[int(UpperCAmelCase_  ) for number in data.strip().split(''','''  )]

  __UpperCAmelCase    :						List[Any]       =						filter_valid_chars(UpperCAmelCase_  )
  for common_word in COMMON_WORDS:
    __UpperCAmelCase    :						Optional[int]       =						filter_common_word(UpperCAmelCase_  ,			UpperCAmelCase_  )
    if len(UpperCAmelCase_  ) == 1:
      break

  __UpperCAmelCase    :						Optional[Any]       =						possibles[0]
  return sum(ord(UpperCAmelCase_  ) for char in decoded_text  )


if __name__ == "__main__":
  print(f"""{solution() = }""")