






'''simple docstring'''
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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


			"""simple docstring"""




			lowercase_	:    Tuple           =     len([g for position, g in enumerate(_UpperCamelCase							) if g == main_target[position]]							)
			return (item, float(_UpperCamelCase							))




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


			"""simple docstring"""




			lowercase_	:    Union[str, Any]           =     random.randint(0						,		len(_UpperCamelCase							) - 1							)
			lowercase_	:    Any           =     parent_a[:random_slice] + parent_a[random_slice:]
			lowercase_	:    Optional[Any]           =     parent_a[:random_slice] + parent_a[random_slice:]
			return (child_a, child_a)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


			"""simple docstring"""




			lowercase_	:    Tuple           =     list(_UpperCamelCase							)
			if random.uniform(0						,		1							) < MUTATION_PROBABILITY:
						lowercase_	:    Optional[Any]           =     random.choice(_UpperCamelCase							)
			return "".join(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		):


			"""simple docstring"""




			lowercase_	:    Union[str, Any]           =     []
			# Generate more children proportionally to the fitness score.
			lowercase_	:    str           =     int(parent_a[1] * 100							) + 1
			lowercase_	:    Dict           =     10 if child_n >= 10 else child_n
			for _ in range(_UpperCamelCase							):
						lowercase_	:    int           =     population_score[random.randint(0						,		_UpperCamelCase							)][0]

						lowercase_	:    Dict           =     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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = True							):


			"""simple docstring"""




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

			# Generate random starting population.
			lowercase_	:    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.
			lowercase_	:    Optional[int]           =     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.
						lowercase_	:    List[Any]           =     [evaluate(_UpperCamelCase						,		_UpperCamelCase							) for item in population]

						# Check if there is a matching evolution.
						lowercase_	:    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.
						lowercase_	:    Union[str, Any]           =     population[: int(N_POPULATION / 3							)]
						population.clear()
						population.extend(_UpperCamelCase							)
						# Normalize population score to be between 0 and 1.
						lowercase_	:    Union[str, Any]           =     [
						    (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__":
	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}"""
	)












'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator


class        _UpperCAmelCase						:




    def __init__(			self  :       Optional[Any]  ,     a  :       Any			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     str(id_			)
       lowercase_	:    List[str]           =     None
       lowercase_	:    Tuple           =     None
       lowercase_	:    Dict           =     []
       lowercase_	:    Union[str, Any]           =     {}  # {vertex:distance}




    def __lt__(			self  :       Optional[Any]  ,     a  :       int			):






       '''simple docstring'''






       return self.key < other.key




    def __repr__(			self  :       Union[str, Any]			):






       '''simple docstring'''






       return self.id




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Optional[int]			):






       '''simple docstring'''






       self.neighbors.append(a			)





    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       int  ,     a  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    int           =     weight




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   graph[a - 1].add_neighbor(graph[b - 1]							)
   graph[b - 1].add_neighbor(graph[a - 1]							)
   # add the edges:
   graph[a - 1].add_edge(graph[b - 1]						,		_UpperCamelCase							)
   graph[b - 1].add_edge(graph[a - 1]						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Any           =     []
   for u in graph:
      lowercase_	:    List[Any]           =     math.inf
      lowercase_	:    str           =     None
   lowercase_	:    Tuple           =     0
   lowercase_	:    Tuple           =     graph[:]
   while q:
      lowercase_	:    List[Any]           =     min(_UpperCamelCase							)
      q.remove(_UpperCamelCase							)
      for v in u.neighbors:
         if (v in q) and (u.edges[v.id] < v.key):
            lowercase_	:    Optional[int]           =     u
            lowercase_	:    Union[str, Any]           =     u.edges[v.id]
   for i in range(1						,		len(_UpperCamelCase							)							):
      a.append((int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)							)
   return a




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   for u in graph:
      lowercase_	:    str           =     math.inf
      lowercase_	:    int           =     None
   lowercase_	:    List[Any]           =     0

   lowercase_	:    str           =     list(_UpperCamelCase							)
   hq.heapify(_UpperCamelCase							)

   while h:
      lowercase_	:    List[Any]           =     hq.heappop(_UpperCamelCase							)
      for v in u.neighbors:
         if (v in h) and (u.edges[v.id] < v.key):
            lowercase_	:    str           =     u
            lowercase_	:    Optional[int]           =     u.edges[v.id]
            hq.heapify(_UpperCamelCase							)

   for i in range(1						,		len(_UpperCamelCase							)							):
      yield (int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""






if __name__ == "__main__":
 import doctest

 doctest.testmod()




