






from collections.abc import Generator
from math import sin


def __A    (      __lowerCAmelCase	)->  Union[str, Any]:





						"""simple docstring"""



						if len(_UpperCAmelCase	) != 32:
												raise ValueError('Input must be of length 32'	)

						_UpperCAmelCase									=			b''
						for i in [3, 2, 1, 0]:
												little_endian += string_aa[8 * i : 8 * i + 8]
						return little_endian


def __A    (      __lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



						if i < 0:
												raise ValueError('Input must be non-negative'	)

						_UpperCAmelCase									=			format(_UpperCAmelCase ,		'08x'	)[-8:]
						_UpperCAmelCase									=			b''
						for i in [3, 2, 1, 0]:
												little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('utf-8'	)
						return little_endian_hex


def __A    (      __lowerCAmelCase	)->  List[str]:





						"""simple docstring"""



						_UpperCAmelCase									=			b''
						for char in message:
												bit_string += format(_UpperCAmelCase ,		'08b'	).encode('utf-8'	)
						_UpperCAmelCase									=			format(len(_UpperCAmelCase	) ,		'064b'	).encode('utf-8'	)

						# Pad bit_string to a multiple of 512 chars
						bit_string += b"1"
						while len(_UpperCAmelCase	) % 512 != 448:
												bit_string += b"0"
						bit_string += to_little_endian(start_len[32:]	) + to_little_endian(start_len[:32]	)

						return bit_string


def __A    (      __lowerCAmelCase	)->  Dict:





						"""simple docstring"""



						if len(_UpperCAmelCase	) % 512 != 0:
												raise ValueError('Input must have length that\'s a multiple of 512'	)

						for pos in range(0 ,		len(_UpperCAmelCase	) ,		512	):
												_UpperCAmelCase									=			bit_string[pos : pos + 512]
												_UpperCAmelCase									=			[]
												for i in range(0 ,		512 ,		32	):
																		block_words.append(int(to_little_endian(block[i : i + 32]	) ,		2	)	)
												yield block_words


def __A    (      __lowerCAmelCase	)->  Tuple:





						"""simple docstring"""



						if i < 0:
												raise ValueError('Input must be non-negative'	)

						_UpperCAmelCase									=			format(_UpperCAmelCase ,		'032b'	)
						_UpperCAmelCase									=			''
						for c in i_str:
												new_str += "1" if c == "0" else "0"
						return int(_UpperCAmelCase ,		2	)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  Optional[int]:





						"""simple docstring"""



						return (a + b) % 2**32


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  Any:





						"""simple docstring"""



						if i < 0:
												raise ValueError('Input must be non-negative'	)
						if shift < 0:
												raise ValueError('Shift must be non-negative'	)
						return ((i << shift) ^ (i >> (32 - shift))) % 2**32


def __A    (      __lowerCAmelCase	)->  Union[str, Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			preprocess(_UpperCAmelCase	)

						_UpperCAmelCase									=			[int(2**32 * abs(sin(i + 1	)	)	) for i in range(64	)]

						# Starting states
						_UpperCAmelCase									=			0X6_7_4_5_2_3_0_1
						_UpperCAmelCase									=			0XE_F_C_D_A_B_8_9
						_UpperCAmelCase									=			0X9_8_B_A_D_C_F_E
						_UpperCAmelCase									=			0X1_0_3_2_5_4_7_6

						_UpperCAmelCase									=			[
						    7,
						    12,
						    17,
						    22,
						    7,
						    12,
						    17,
						    22,
						    7,
						    12,
						    17,
						    22,
						    7,
						    12,
						    17,
						    22,
						    5,
						    9,
						    14,
						    20,
						    5,
						    9,
						    14,
						    20,
						    5,
						    9,
						    14,
						    20,
						    5,
						    9,
						    14,
						    20,
						    4,
						    11,
						    16,
						    23,
						    4,
						    11,
						    16,
						    23,
						    4,
						    11,
						    16,
						    23,
						    4,
						    11,
						    16,
						    23,
						    6,
						    10,
						    15,
						    21,
						    6,
						    10,
						    15,
						    21,
						    6,
						    10,
						    15,
						    21,
						    6,
						    10,
						    15,
						    21,
						]

						# Process bit string in chunks, each with 16 32-char words
						for block_words in get_block_words(_UpperCAmelCase	):
												_UpperCAmelCase									=			aa
												_UpperCAmelCase									=			ba
												_UpperCAmelCase									=			ca
												_UpperCAmelCase									=			da

												# Hash current chunk
												for i in range(64	):
																		if i <= 15:
																								# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
																								_UpperCAmelCase									=			d ^ (b & (c ^ d))
																								_UpperCAmelCase									=			i
																		elif i <= 31:
																								# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
																								_UpperCAmelCase									=			c ^ (d & (b ^ c))
																								_UpperCAmelCase									=			(5 * i + 1) % 16
																		elif i <= 47:
																								_UpperCAmelCase									=			b ^ c ^ d
																								_UpperCAmelCase									=			(3 * i + 5) % 16
																		else:
																								_UpperCAmelCase									=			c ^ (b | not_aa(_UpperCAmelCase	))
																								_UpperCAmelCase									=			(7 * i) % 16
																		_UpperCAmelCase									=			(f + a + added_consts[i] + block_words[g]) % 2**32
																		_UpperCAmelCase									=			d
																		_UpperCAmelCase									=			c
																		_UpperCAmelCase									=			b
																		_UpperCAmelCase									=			sum_aa(_UpperCAmelCase ,		left_rotate_aa(_UpperCAmelCase ,		shift_amounts[i]	)	)

												# Add hashed chunk to running total
												_UpperCAmelCase									=			sum_aa(_UpperCAmelCase ,		_UpperCAmelCase	)
												_UpperCAmelCase									=			sum_aa(_UpperCAmelCase ,		_UpperCAmelCase	)
												_UpperCAmelCase									=			sum_aa(_UpperCAmelCase ,		_UpperCAmelCase	)
												_UpperCAmelCase									=			sum_aa(_UpperCAmelCase ,		_UpperCAmelCase	)

						_UpperCAmelCase									=			reformat_hex(_UpperCAmelCase	) + reformat_hex(_UpperCAmelCase	) + reformat_hex(_UpperCAmelCase	) + reformat_hex(_UpperCAmelCase	)
						return digest


if __name__ == "__main__":
		import doctest

		doctest.testmod()





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__snake_case						:Optional[int]      		=     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						:List[str]      		=     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						:List[Any]      		=     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						:Optional[int]      		=     (
     '''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
 )
 __snake_case						:List[Any]      		=     list(
     ''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
     '''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
 )
 __snake_case		,__snake_case		,__snake_case						:Dict      		=     basic(target_str, genes_list)
 print(
     f'\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'
 )





