



from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__snake_case						:Dict						=					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.
__snake_case						:Optional[Any]						=					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.
__snake_case						:int						=					0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    len([g for position, g in enumerate(_UpperCAmelCase    ) if g == main_target[position]]    )
    return (item, float(_UpperCAmelCase    ))





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    random.randint(0       ,			len(_UpperCAmelCase    ) - 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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    list(_UpperCAmelCase    )
    if random.uniform(0       ,			1    ) < MUTATION_PROBABILITY:
        __a							=    random.choice(_UpperCAmelCase    )
    return "".join(_UpperCAmelCase    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			):
    __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(_UpperCAmelCase    ):
        __a							=    population_score[random.randint(0       ,			_UpperCAmelCase    )][0]

        __a	,       __a							=    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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase = True    ):

    # Verify if N_POPULATION is bigger than N_SELECTED
    if N_POPULATION < N_SELECTED:
        __a							=    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.
    __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(_UpperCAmelCase    )

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

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










from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class    _A	:
       UpperCamelCase__  :   int
       UpperCamelCase__  :   TreeNode | None						 =						None
       UpperCamelCase__  :   TreeNode | None						 =						None


__snake_case						:Optional[Any]						=					namedtuple('''CoinsDistribResult''', '''moves excess''')





def 					__snake_case     (      _UpperCAmelCase    ):

    if root is None:
        return 0

    # Validation
    def count_nodes(_UpperCAmelCase    ) -> int:
        if node is None:
            return 0

        return count_nodes(node.left    ) + count_nodes(node.right    ) + 1

    def count_coins(_UpperCAmelCase    ) -> int:
        if node is None:
            return 0

        return count_coins(node.left    ) + count_coins(node.right    ) + node.data

    if count_nodes(_UpperCAmelCase    ) != count_coins(_UpperCAmelCase    ):
        raise ValueError('''The nodes number should be same as the number of coins'''    )

    # Main calculation
    def get_distrib(_UpperCAmelCase    ) -> CoinsDistribResult:

        if node is None:
            return CoinsDistribResult(0       ,			1    )

        __a	,       __a							=    get_distrib(node.left    )
        __a	,       __a							=    get_distrib(node.right    )

        __a							=    1 - left_distrib_excess
        __a							=    1 - right_distrib_excess

        __a							=    (
            left_distrib_moves
            + right_distrib_moves
            + abs(_UpperCAmelCase    )
            + abs(_UpperCAmelCase    )
        )
        __a							=    node.data - coins_to_left - coins_to_right

        return CoinsDistribResult(_UpperCAmelCase       ,			_UpperCAmelCase    )

    return get_distrib(_UpperCAmelCase    )[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()





