



"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate



@dataclass(frozen=__UpperCAmelCase				)
class    SCREAMING_SNAKE_CASE__						(      __UpperCAmelCase				):

   '''simple docstring'''





   __lowerCAmelCase						:     Any		=		field(default='language-modeling'   ,    metadata={'include_in_asdict_even_if_is_default': True}				)

   __lowerCAmelCase						:     Tuple		=		Features({'text': Value('string'				)}				)
   __lowerCAmelCase						:     Union[str, Any]		=		Features({}				)
   __lowerCAmelCase						:     List[str]		=		'text'

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    return {self.text_column: "text"}





"""simple docstring"""
from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
A:     Union[str, Any]										=	2_0_0
# 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:     int										=	5_0
# 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:     Tuple										=	0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	str		):
 UpperCAmelCase			:      Tuple			=			len([g for position, g in enumerate(UpperCamelCase		) if g == main_target[position]]		)
 return (item, float(UpperCamelCase		))

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	str		):
 UpperCAmelCase			:      List[str]			=			random.randint(0    ,	len(UpperCamelCase		) - 1		)
 UpperCAmelCase			:      List[str]			=			parent_a[:random_slice] + parent_a[random_slice:]
 UpperCAmelCase			:      List[str]			=			parent_a[:random_slice] + parent_a[random_slice:]
 return (child_a, child_a)

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	list[str]		):
 UpperCAmelCase			:      str			=			list(UpperCamelCase		)
 if random.uniform(0    ,	1		) < MUTATION_PROBABILITY:
  UpperCAmelCase			:      int			=			random.choice(UpperCamelCase		)
 return "".join(UpperCamelCase		)

def    _snake_case		(		UpperCamelCase		:	tuple[str, float]    ,	UpperCamelCase		:	list[tuple[str, float]]    ,	UpperCamelCase		:	list[str]    ,	):
 UpperCAmelCase			:      Optional[Any]			=			[]
 # Generate more children proportionally to the fitness score.
 UpperCAmelCase			:      Optional[Any]			=			int(parent_a[1] * 100		) + 1
 UpperCAmelCase			:      List[str]			=			10 if child_n >= 10 else child_n
 for _ in range(UpperCamelCase		):
  UpperCAmelCase			:      List[str]			=			population_score[random.randint(0    ,	UpperCamelCase		)][0]

  UpperCAmelCase			,	UpperCAmelCase			:      Any			=			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		:	str    ,	UpperCamelCase		:	list[str]    ,	UpperCamelCase		:	bool = True		):

 # Verify if N_POPULATION is bigger than N_SELECTED
 if N_POPULATION < N_SELECTED:
  UpperCAmelCase			:      Dict			=			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.
 UpperCAmelCase			:      str			=			sorted({c for c in target if c not in genes}		)
 if not_in_genes_list:
  UpperCAmelCase			:      Optional[Any]			=			F"{not_in_genes_list} is not in genes list, evolution cannot converge"
  raise ValueError(UpperCamelCase		)

 # Generate random starting population.
 UpperCAmelCase			:      Optional[int]			=			[]
 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.
 UpperCAmelCase			,	UpperCAmelCase			:      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.
  UpperCAmelCase			:      str			=			[evaluate(UpperCamelCase    ,	UpperCamelCase		) for item in population]

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