





"""simple docstring"""






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, 1_000))

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       tuple[str, float]:






       """simple docstring"""




       _UpperCAmelCase     =     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,SCREAMING_SNAKE_CASE							)		->       tuple[str, str]:






       """simple docstring"""




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

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




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

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,)		->       list[str]:






       """simple docstring"""




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

              _UpperCAmelCase     =     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,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE = True							)		->       tuple[int, int, str]:






       """simple docstring"""




       if N_POPULATION < N_SELECTED:
              _UpperCAmelCase     =     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.
       _UpperCAmelCase     =     sorted({c for c in target if c not in genes}							)
       if not_in_genes_list:
              _UpperCAmelCase     =     F"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
              raise ValueError(SCREAMING_SNAKE_CASE							)

       # Generate random starting population.
       _UpperCAmelCase     =     []
       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.
       _UpperCAmelCase     =     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.
              _UpperCAmelCase     =     [evaluate(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							) for item in population]

              # Check if there is a matching evolution.
              _UpperCAmelCase     =     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.
              _UpperCAmelCase     =     population[: int(N_POPULATION / 3							)]
              population.clear()
              population.extend(SCREAMING_SNAKE_CASE							)
              # Normalize population score to be between 0 and 1.
              _UpperCAmelCase     =     [
                  (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_  			=							basic(target_str, genes_list)
    print(
        F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
    )













"""simple docstring"""






def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       list:






       """simple docstring"""




       _UpperCAmelCase     =     False
       while is_sorted is False:  # Until all the indices are traversed keep looping
              _UpperCAmelCase     =     True
              for i in range(0,len(SCREAMING_SNAKE_CASE							) - 1,2							):  # iterating over all even indices
                     if input_list[i] > input_list[i + 1]:
                            _UpperCAmelCase    ,							_UpperCAmelCase     =     input_list[i + 1], input_list[i]
                            # swapping if elements not in order
                            _UpperCAmelCase     =     False

              for i in range(1,len(SCREAMING_SNAKE_CASE							) - 1,2							):  # iterating over all odd indices
                     if input_list[i] > input_list[i + 1]:
                            _UpperCAmelCase    ,							_UpperCAmelCase     =     input_list[i + 1], input_list[i]
                            # swapping if elements not in order
                            _UpperCAmelCase     =     False
       return input_list


if __name__ == "__main__":
    print('''Enter list to be sorted''')
    lowerCAmelCase_			=							[int(x) for x in input().split()]
    # inputing elements of the list in one line
    lowerCAmelCase_			=							odd_even_sort(input_list)
    print('''The sorted list is''')
    print(sorted_list)






