




from __future__ import annotations

import random

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




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				int:
      _UpperCAmelCase      =			len([g for position, g in enumerate(_lowerCamelCase       ) if g == main_target[position]]       )
      return (item, float(_lowerCamelCase       ))




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				int:
      _UpperCAmelCase      =			random.randint(0			,     len(_lowerCamelCase       ) - 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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Optional[Any]:
      _UpperCAmelCase      =			list(_lowerCamelCase       )
      if random.uniform(0			,     1       ) < MUTATION_PROBABILITY:
            _UpperCAmelCase      =			random.choice(_lowerCamelCase       )
      return "".join(_lowerCamelCase       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     )						->				Optional[int]:
      _UpperCAmelCase      =			[]
      # Generate more children proportionally to the fitness score.
      _UpperCAmelCase      =			int(parent_a[1] * 1_0_0       ) + 1
      _UpperCAmelCase      =			1_0 if child_n >= 1_0 else child_n
      for _ in range(_lowerCamelCase       ):
            _UpperCAmelCase      =			population_score[random.randint(0			,     _lowerCamelCase       )][0]

            _UpperCAmelCase      =			crossover(parent_a[0]			,     _lowerCamelCase       )
            # Append new string to the population list.
            pop.append(mutate(_lowerCamelCase			,     _lowerCamelCase       )       )
            pop.append(mutate(_lowerCamelCase			,     _lowerCamelCase       )       )
      return pop




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case = True       )						->				Union[str, Any]:


      if N_POPULATION < N_SELECTED:
            _UpperCAmelCase      =			f"{N_POPULATION} must be bigger than {N_SELECTED}"
            raise ValueError(_lowerCamelCase       )
      # 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(_lowerCamelCase       )

      # Generate random starting population.
      _UpperCAmelCase      =			[]
      for _ in range(_lowerCamelCase       ):
            population.append("""""".join([random.choice(_lowerCamelCase       ) for i in range(len(_lowerCamelCase       )       )]       )       )

      # 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(_lowerCamelCase       )

            # 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(_lowerCamelCase			,     _lowerCamelCase       ) for item in population]

            # Check if there is a matching evolution.
            _UpperCAmelCase      =			sorted(_lowerCamelCase			,     key=lambda snake_case       : x[1]			,     reverse=_lowerCamelCase       )
            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.
            _UpperCAmelCase      =			population[: int(N_POPULATION / 3       )]
            population.clear()
            population.extend(_lowerCamelCase       )
            # Normalize population score to be between 0 and 1.
            _UpperCAmelCase      =			[
                (item, score / len(_lowerCamelCase       )) for item, score in population_score
            ]

            # This is selection
            for i in range(_lowerCamelCase       ):
                  population.extend(select(population_score[int(_lowerCamelCase       )]			,     _lowerCamelCase			,     _lowerCamelCase       )       )
                  # 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(_lowerCamelCase       ) > N_POPULATION:
                        break


if __name__ == "__main__":
    a													=			(
        "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
    )
    a													=			list(
        " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
        "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
    )
    a													=			basic(target_str, genes_list)
    print(
        F'\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'
    )





import itertools
import string
from collections.abc import Generator, Iterable




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Generator[tuple[str, ...], None, None]:
						_UpperCAmelCase      =			iter(snake_case       )
						while True:
												_UpperCAmelCase      =			tuple(itertools.islice(snake_case			,     snake_case       )       )
												if not chunk:
																		return
												yield chunk




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				str:
						_UpperCAmelCase      =			"""""".join([c.upper() for c in dirty if c in string.ascii_letters]       )
						_UpperCAmelCase      =			""""""

						if len(snake_case       ) < 2:
												return dirty

						for i in range(len(snake_case       ) - 1       ):
												clean += dirty[i]

												if dirty[i] == dirty[i + 1]:
																		clean += "X"

						clean += dirty[-1]

						if len(snake_case       ) & 1:
												clean += "X"

						return clean




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				list[str]:
						# I and J are used interchangeably to allow
						# us to use a 5x5 table (25 letters)
						_UpperCAmelCase      =			"""ABCDEFGHIKLMNOPQRSTUVWXYZ"""
						# we're using a list instead of a '2d' array because it makes the math
						# for setting up the table and doing the actual encoding/decoding simpler
						_UpperCAmelCase      =			[]

						# copy key chars into the table if they are in `alphabet` ignoring duplicates
						for char in key.upper():
												if char not in table and char in alphabet:
																		table.append(snake_case       )

    # fill the rest of the table in with the remaining alphabet chars
						for char in alphabet:
												if char not in table:
																		table.append(snake_case       )

						return table




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				str:
						_UpperCAmelCase      =			generate_table(snake_case       )
						_UpperCAmelCase      =			prepare_input(snake_case       )
						_UpperCAmelCase      =			""""""

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(snake_case			,     2       ):
												_UpperCAmelCase    ,					_UpperCAmelCase      =			divmod(table.index(snake_case       )			,     5       )
												_UpperCAmelCase    ,					_UpperCAmelCase      =			divmod(table.index(snake_case       )			,     5       )

												if rowa == rowa:
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
												elif cola == cola:
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												else:  # rectangle
																		ciphertext += table[rowa * 5 + cola]
																		ciphertext += table[rowa * 5 + cola]

						return ciphertext




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				str:
						_UpperCAmelCase      =			generate_table(snake_case       )
						_UpperCAmelCase      =			""""""

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(snake_case			,     2       ):
												_UpperCAmelCase    ,					_UpperCAmelCase      =			divmod(table.index(snake_case       )			,     5       )
												_UpperCAmelCase    ,					_UpperCAmelCase      =			divmod(table.index(snake_case       )			,     5       )

												if rowa == rowa:
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
												elif cola == cola:
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
												else:  # rectangle
																		plaintext += table[rowa * 5 + cola]
																		plaintext += table[rowa * 5 + cola]

						return plaintext