





from __future__ import annotations

import random

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str		,   __UpperCAmelCase:			str      )      ->	tuple[str, float]:
    UpperCamelCase__							:				List[Any]          =      len([g for position, g in enumerate(_lowerCamelCase      ) if g == main_target[position]]      )
    return (item, float(_lowerCamelCase      ))




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str		,   __UpperCAmelCase:			str      )      ->	tuple[str, str]:
    UpperCamelCase__							:				Dict          =      random.randint(0		,   len(_lowerCamelCase      ) - 1      )
    UpperCamelCase__							:				Tuple          =      parent_a[:random_slice] + parent_a[random_slice:]
    UpperCamelCase__							:				Optional[int]          =      parent_a[:random_slice] + parent_a[random_slice:]
    return (child_a, child_a)




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str		,   __UpperCAmelCase:			list[str]      )      ->	str:
    UpperCamelCase__							:				Optional[Any]          =      list(_lowerCamelCase      )
    if random.uniform(0		,   1      ) < MUTATION_PROBABILITY:
        UpperCamelCase__							:				Optional[int]          =      random.choice(_lowerCamelCase      )
    return "".join(_lowerCamelCase      )




def 		lowerCAmelCase_    (  __UpperCAmelCase:			tuple[str, float]		,   __UpperCAmelCase:			list[tuple[str, float]]		,   __UpperCAmelCase:			list[str]		,   )      ->	list[str]:
    UpperCamelCase__							:				Optional[Any]          =      []
    # Generate more children proportionally to the fitness score.
    UpperCamelCase__							:				Optional[Any]          =      int(parent_a[1] * 100      ) + 1
    UpperCamelCase__							:				Dict          =      10 if child_n >= 10 else child_n
    for _ in range(_lowerCamelCase      ):
        UpperCamelCase__							:				Union[str, Any]          =      population_score[random.randint(0		,   _lowerCamelCase      )][0]

        UpperCamelCase__							:				int          =      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 		lowerCAmelCase_    (  __UpperCAmelCase:			str		,   __UpperCAmelCase:			list[str]		,   __UpperCAmelCase:			bool = True      )      ->	tuple[int, int, str]:

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

    # Generate random starting population.
    UpperCamelCase__							:				Optional[int]          =      []
    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__							:				Tuple          =      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__							:				Dict          =      [evaluate(_lowerCamelCase		,   _lowerCamelCase      ) for item in population]

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


"""simple docstring"""




import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


_a      :		Dict           =   datasets.utils.logging.get_logger(__name__)
@dataclass
class 						__A							( datasets.BuilderConfig		):
     _UpperCamelCase							:			int							=		10_000
     _UpperCamelCase							:			Optional[List[str]]							=		None
     _UpperCamelCase							:			Optional[datasets.Features]							=		None







class 						__A							( datasets.ArrowBasedBuilder		):
     _UpperCamelCase							:			List[str]							=		ParquetConfig






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






     def     __A  (		self				,     a__  ):
       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}"  )
       _lowerCAmelCase						:       Optional[Any]  			=     dl_manager.download_and_extract(self.config.data_files  )
       if isinstance(a__				,     (str, list, tuple)  ):
         _lowerCAmelCase						:       Any  			=     data_files
         if isinstance(a__				,     a__  ):
           _lowerCAmelCase						:       Tuple  			=     [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _lowerCAmelCase						:       Any  			=     [dl_manager.iter_files(a__  ) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,     gen_kwargs={"""files""": files}  )]
       _lowerCAmelCase						:       Optional[Any]  			=     []
       for split_name, files in data_files.items():
         if isinstance(a__				,     a__  ):
           _lowerCAmelCase						:       Dict  			=     [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _lowerCAmelCase						:       Tuple  			=     [dl_manager.iter_files(a__  ) for file in files]
         # Infer features is they are stoed in the arrow schema
         if self.info.features is None:
           for file in itertools.chain.from_iterable(a__  ):
             with open(a__				,     """rb"""  ) as f:
               _lowerCAmelCase						:       Optional[Any]  			=     datasets.Features.from_arrow_schema(pq.read_schema(a__  )  )
             break
         splits.append(datasets.SplitGenerator(name=a__				,     gen_kwargs={"""files""": files}  )  )
       return splits






     def     __A  (		self				,     a__  ):
       if self.info.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
         _lowerCAmelCase						:       Optional[int]  			=     table_cast(a__				,     self.info.features.arrow_schema  )
       return pa_table






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.info.features.arrow_schema if self.info.features is not None else None
       if self.info.features is not None and self.config.columns is not None:
         if sorted(field.name for field in schema  ) != sorted(self.config.columns  ):
           raise ValueError(
               F"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"  )
       for file_idx, file in enumerate(itertools.chain.from_iterable(a__  )  ):
         with open(a__				,     """rb"""  ) as f:
           _lowerCAmelCase						:       Tuple  			=     pq.ParquetFile(a__  )
           try:
             for batch_idx, record_batch in enumerate(
                 parquet_file.iter_batches(batch_size=self.config.batch_size				,     columns=self.config.columns  )  ):
               _lowerCAmelCase						:       Any  			=     pa.Table.from_batches([record_batch]  )
               # Uncomment for debugging (will print the Arrow table size and elements)
               # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
               # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
               yield F"{file_idx}_{batch_idx}", self._cast_table(a__  )
           except ValueError as e:
             logger.error(F"Failed to read file '{file}' with error {type(a__  )}: {e}"  )
             raise




