



from __future__ import annotations

import random

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

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				__a       =		len([g for position, g in enumerate(_SCREAMING_SNAKE_CASE    ) if g == main_target[position]]    )
				return (item, float(_SCREAMING_SNAKE_CASE    ))

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				__a       =		random.randint(0				,     len(_SCREAMING_SNAKE_CASE    ) - 1    )
				__a       =		parent_a[:random_slice] + parent_a[random_slice:]
				__a       =		parent_a[:random_slice] + parent_a[random_slice:]
				return (child_a, child_a)

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			list[str]    ):
				"""simple docstring"""







				__a       =		list(_SCREAMING_SNAKE_CASE    )
				if random.uniform(0				,     1    ) < MUTATION_PROBABILITY:
								__a       =		random.choice(_SCREAMING_SNAKE_CASE    )
				return "".join(_SCREAMING_SNAKE_CASE    )

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			tuple[str, float]				,     _SCREAMING_SNAKE_CASE       :			list[tuple[str, float]]				,     _SCREAMING_SNAKE_CASE       :			list[str]				,     ):
				"""simple docstring"""







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

								__a     ,					__a       =		crossover(parent_a[0]				,     _SCREAMING_SNAKE_CASE    )
								# Append new string to the population list.
								pop.append(mutate(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )    )
								pop.append(mutate(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )    )
				return pop

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			list[str]				,     _SCREAMING_SNAKE_CASE       :			bool = True    ):
				"""simple docstring"""







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

				# Generate random starting population.
				__a       =		[]
				for _ in range(_SCREAMING_SNAKE_CASE    ):
								population.append("""""".join([random.choice(_SCREAMING_SNAKE_CASE    ) for i in range(len(_SCREAMING_SNAKE_CASE    )    )]    )    )

				# Just some logs to know what the algorithms is doing.
				__a     ,					__a       =		0, 0

				# This loop will end when we find a perfect match for our target.
				while True:
								generation += 1
								total_population += len(_SCREAMING_SNAKE_CASE    )

								# 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.
								__a       =		[evaluate(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    ) for item in population]

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

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


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





from __future__ import annotations

from collections import Counter
from random import random






class    SCREAMING_SNAKE_CASE			:





							def __init__(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		{}





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		{}





							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		str      ,				__lowercase		:		str      ,				__lowercase		:		float	):

											'''simple docstring'''
											if nodea not in self.connections:
															self.add_node(__lowercase	)
											if nodea not in self.connections:
															self.add_node(__lowercase	)
											__a       =		probability





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

											'''simple docstring'''
											return list(self.connections	)







							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		0
											__a       =		random()

											for dest in self.connections[node]:
															current_probability += self.connections[node][dest]
															if current_probability > random_value:
																			return dest
											return ""



def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			list[tuple[str, str, float]]				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a       =		MarkovChainGraphUndirectedUnweighted()

				for nodea, nodea, probability in transitions:
								graph.add_transition_probability(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

				__a       =		Counter(graph.get_nodes()    )
				__a       =		start

				for _ in range(_SCREAMING_SNAKE_CASE    ):
								__a       =		graph.transition(_SCREAMING_SNAKE_CASE    )
								visited[node] += 1

				return visited


if __name__ == "__main__":
						import doctest

						doctest.testmod()
