



from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_lowercase     							=						2_00
# 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     							=						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.
_lowercase     							=						0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->tuple[str, float]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[int]								=		len([g for position, g in enumerate(UpperCamelCase ) if g == main_target[position]] )
			return (item, float(UpperCamelCase ))






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->tuple[str, str]:



			'''simple docstring'''







			lowerCamelCase__ :      str								=		random.randint(0      ,			len(UpperCamelCase ) - 1 )
			lowerCamelCase__ :      Optional[Any]								=		parent_a[:random_slice] + parent_a[random_slice:]
			lowerCamelCase__ :      Optional[int]								=		parent_a[:random_slice] + parent_a[random_slice:]
			return (child_a, child_a)






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      list[str] )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		list(UpperCamelCase )
			if random.uniform(0      ,			1 ) < MUTATION_PROBABILITY:
						lowerCamelCase__ :      Union[str, Any]								=		random.choice(UpperCamelCase )
			return "".join(UpperCamelCase )






def      _A				(UpperCamelCase		:      tuple[str, float]      ,			UpperCamelCase		:      list[tuple[str, float]]      ,			UpperCamelCase		:      list[str]      ,			)  ->list[str]:



			'''simple docstring'''







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

						lowerCamelCase__							,lowerCamelCase__ :      str								=		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      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      list[str]      ,			UpperCamelCase		:      bool = True )  ->tuple[int, int, str]:



			'''simple docstring'''







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

			# Generate random starting population.
			lowerCamelCase__ :      Any								=		[]
			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.
			lowerCamelCase__							,lowerCamelCase__ :      Union[str, 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.
						lowerCamelCase__ :      Union[str, Any]								=		[evaluate(UpperCamelCase      ,			UpperCamelCase ) for item in population]

						# Check if there is a matching evolution.
						lowerCamelCase__ :      List[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.
						lowerCamelCase__ :      Optional[int]								=		population[: int(N_POPULATION / 3 )]
						population.clear()
						population.extend(UpperCamelCase )
						# Normalize population score to be between 0 and 1.
						lowerCamelCase__ :      int								=		[
						    (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     							=						(
				    '''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
				)
				_lowercase     							=						list(
				    ''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
				    '''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
				)
				_lowercase							,				_lowercase							,				_lowercase     							=						basic(target_str, genes_list)
				print(
				    F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
				)








from __future__ import annotations






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      list[str] | None = None )  ->list[list[str]]:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		word_bank or []
			# create a table
			lowerCamelCase__ :      int								=		len(UpperCamelCase ) + 1

			lowerCamelCase__ :      list[list[list[str]]]								=		[]
			for _ in range(UpperCamelCase ):
						table.append([] )
			# seed value
			lowerCamelCase__ :      Tuple								=		[[]]  # because empty string has empty combination

			# iterate through the indices
			for i in range(UpperCamelCase ):
						# condition
						if table[i] != []:
									for word in word_bank:
												# slice condition
												if target[i : i + len(UpperCamelCase )] == word:
															lowerCamelCase__ :      list[list[str]]								=		[
															    [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(UpperCamelCase )] += new_combinations

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

			return table[len(UpperCamelCase )]


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'''],
				    )
				)



