




from __future__ import annotations

import random

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



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  lowercase			:List[Any]							=  len([g for position, g in enumerate(lowerCamelCase      ) if g == main_target[position]]      )
  return (item, float(lowerCamelCase      ))



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  lowercase			:Optional[int]							=  random.randint(0,	len(lowerCamelCase      ) - 1      )
  lowercase			:Union[str, Any]							=  parent_a[:random_slice] + parent_a[random_slice:]
  lowercase			:Union[str, Any]							=  parent_a[:random_slice] + parent_a[random_slice:]
  return (child_a, child_a)



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  lowercase			:List[Any]							=  list(lowerCamelCase      )
  if random.uniform(0,	1      ) < MUTATION_PROBABILITY:
    lowercase			:str							=  random.choice(lowerCamelCase      )
  return "".join(lowerCamelCase      )



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	):
  lowercase			:Tuple							=  []
  # Generate more children proportionally to the fitness score.
  lowercase			:str							=  int(parent_a[1] * 100      ) + 1
  lowercase			:int							=  10 if child_n >= 10 else child_n
  for _ in range(lowerCamelCase      ):
    lowercase			:Optional[Any]							=  population_score[random.randint(0,	lowerCamelCase      )][0]

    lowercase		,			lowercase			:Any							=  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 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase = True      ):

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

  # Generate random starting population.
  lowercase			:List[str]							=  []
  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.
  lowercase		,			lowercase			:Optional[Any]							=  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.
    lowercase			:List[str]							=  [evaluate(lowerCamelCase,	lowerCamelCase      ) for item in population]

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









import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast




@dataclass
class       __lowerCAmelCase				(							datasets.BuilderConfig):
       _a           =  None







class       __lowerCAmelCase				(							datasets.ArrowBasedBuilder):
       _a           =  PandasConfig






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         return datasets.DatasetInfo(features=self.config.features )






       def     SCREAMING_SNAKE_CASE			(      self:    str				,    _lowerCAmelCase:    List[str] ):
         if not self.config.data_files:
           raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}" )
         lowercase			:int							=  dl_manager.download_and_extract(self.config.data_files )
         if isinstance(_lowerCAmelCase				,    (str, list, tuple) ):
           lowercase			:Union[str, Any]							=  data_files
           if isinstance(_lowerCAmelCase				,    _lowerCAmelCase ):
             lowercase			:str							=  [files]
           # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
           lowercase			:Optional[int]							=  [dl_manager.iter_files(_lowerCAmelCase ) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,    gen_kwargs={"files": files} )]
         lowercase			:List[Any]							=  []
         for split_name, files in data_files.items():
           if isinstance(_lowerCAmelCase				,    _lowerCAmelCase ):
             lowercase			:Optional[Any]							=  [files]
           # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
           lowercase			:Optional[Any]							=  [dl_manager.iter_files(_lowerCAmelCase ) for file in files]
           splits.append(datasets.SplitGenerator(name=_lowerCAmelCase				,    gen_kwargs={"files": files} ) )
         return splits






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    pa.Table ):
         if self.config.features is not None:
           # more expensive cast to support nested features with keys in a different order
           # allows str <-> int/float or str to Audio for example
           lowercase			:Any							=  table_cast(_lowerCAmelCase				,    self.config.features.arrow_schema )
         return pa_table






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    _lowerCAmelCase:    Union[str, Any] ):
         for i, file in enumerate(itertools.chain.from_iterable(_lowerCAmelCase ) ):
           with open(_lowerCAmelCase				,    "rb" ) as f:
             lowercase			:int							=  pa.Table.from_pandas(pd.read_pickle(_lowerCAmelCase ) )
             yield i, self._cast_table(_lowerCAmelCase )



