

from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCAmelCase__	   = 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.
lowerCAmelCase__	   = 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.
lowerCAmelCase__	   = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							str      )			->      tuple[str, float]:




				'''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      )			->      tuple[str, 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]      )			->      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]	,       )			->      list[str]:




				'''simple docstring'''





				A__        =			[]
				# Generate more children proportionally to the fitness score.
				A__        =			int(parent_a[1] * 1_0_0      ) + 1
				A__        =			1_0 if child_n >= 1_0 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      )			->      tuple[int, int, str]:




				'''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 % 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.
								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



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							list[str] | None = None      )			->      list[list[str]]:




				'''simple docstring'''





				A__        =			word_bank or []
				# create a table
				A__        =			len(SCREAMING_SNAKE_CASE_      ) + 1

				A__        =			[]
				for _ in range(SCREAMING_SNAKE_CASE_      ):
								table.append([]      )
				# seed value
				A__        =			[[]]  # because empty string has empty combination

				# iterate through the indices
				for i in range(SCREAMING_SNAKE_CASE_      ):
								# condition
								if table[i] != []:
												for word in word_bank:
																# slice condition
																if target[i : i + len(SCREAMING_SNAKE_CASE_      )] == word:
																				A__        =			[
																				    [word, *way] for way in table[i]
																				]
																				# adds the word to every combination the current position holds
																				# now,push that combination to the table[i+len(word)]
																				table[i + len(SCREAMING_SNAKE_CASE_      )] += new_combinations

    # combinations are in reverse order so reverse for better output
				for combination in table[len(SCREAMING_SNAKE_CASE_      )]:
								combination.reverse()

				return table[len(SCREAMING_SNAKE_CASE_      )]


if __name__ == "__main__":
	print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
	print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
	print(
	    all_construct(
	        """hexagonosaurus""",
	        ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
	    )
	)

