






"""simple docstring"""





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__lowerCamelCase							=  2_00
# 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.
__lowerCamelCase							=  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.
__lowerCamelCase							=  0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))



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



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    str  )    ->							tuple[str, str]:
						__magic_name__:	Tuple							=       random.randint(0      ,   len(__UpperCAmelCase  ) - 1  )
						__magic_name__:	Optional[Any]							=       parent_a[:random_slice] + parent_a[random_slice:]
						__magic_name__:	Optional[Any]							=       parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    list[str]  )    ->							str:
						__magic_name__:	Union[str, Any]							=       list(__UpperCAmelCase  )
						if random.uniform(0      ,   1  ) < MUTATION_PROBABILITY:
												__magic_name__:	Any							=       random.choice(__UpperCAmelCase  )
						return "".join(__UpperCAmelCase  )



def   a    (     __UpperCAmelCase      :    tuple[str, float]      ,   __UpperCAmelCase      :    list[tuple[str, float]]      ,   __UpperCAmelCase      :    list[str]      ,   )    ->							list[str]:
						__magic_name__:	Union[str, Any]							=       []
						# Generate more children proportionally to the fitness score.
						__magic_name__:	List[str]							=       int(parent_a[1] * 1_0_0  ) + 1
						__magic_name__:	Union[str, Any]							=       1_0 if child_n >= 1_0 else child_n
						for _ in range(__UpperCAmelCase  ):
												__magic_name__:	Union[str, Any]							=       population_score[random.randint(0      ,   __UpperCAmelCase  )][0]

												__magic_name__,			__magic_name__:	List[str]							=       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   a    (     __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:
												__magic_name__:	Any							=       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.
						__magic_name__:	Dict							=       sorted({c for c in target if c not in genes}  )
						if not_in_genes_list:
												__magic_name__:	List[str]							=       f'{not_in_genes_list} is not in genes list, evolution cannot converge'
												raise ValueError(__UpperCAmelCase  )

						# Generate random starting population.
						__magic_name__:	Optional[Any]							=       []
						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.
						__magic_name__,			__magic_name__:	Optional[int]							=       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.
												__magic_name__:	Tuple							=       [evaluate(__UpperCAmelCase      ,   __UpperCAmelCase  ) for item in population]

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







'''simple docstring'''

# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
 import torch
class     __lowerCAmelCase		(TensorFormatter[Mapping, """torch.Tensor""", Mapping]	):





      '''simple docstring'''






      def __init__(self       :       Optional[Any]  ,		UpperCamelCase       :       Tuple=None  ,		**UpperCamelCase       :       List[str]					):

             '''simple docstring'''







             super().__init__(features=UpperCamelCase					)
             lowercase__      =					torch_tensor_kwargs
             import torch  # noqa import torch at initialization



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       Union[str, Any]					):

             '''simple docstring'''







             import torch

             if isinstance(UpperCamelCase  ,		UpperCamelCase					) and column:
                    if all(
                        isinstance(UpperCamelCase  ,		torch.Tensor					) and x.shape == column[0].shape and x.dtype == column[0].dtype
                        for x in column					):
                           return torch.stack(UpperCamelCase					)
             return column



      def  UpperCamelCase__						(self       :       List[Any]  ,		UpperCamelCase       :       Optional[Any]					):

             '''simple docstring'''







             import torch

             if isinstance(UpperCamelCase  ,		(str, bytes, type(UpperCamelCase					))					):
                    return value
             elif isinstance(UpperCamelCase  ,		(np.character, np.ndarray)					) and np.issubdtype(value.dtype  ,		np.character					):
                    return value.tolist()

             lowercase__      =					{}

             if isinstance(UpperCamelCase  ,		(np.number, np.ndarray)					) and np.issubdtype(value.dtype  ,		np.integer					):
                    lowercase__      =					{'''dtype''': torch.intaa}
             elif isinstance(UpperCamelCase  ,		(np.number, np.ndarray)					) and np.issubdtype(value.dtype  ,		np.floating					):
                    lowercase__      =					{'''dtype''': torch.floataa}
             elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                    import PIL.Image

                    if isinstance(UpperCamelCase  ,		PIL.Image.Image					):
                           lowercase__      =					np.asarray(UpperCamelCase					)
             return torch.tensor(UpperCamelCase  ,		**{**default_dtype, **self.torch_tensor_kwargs}					)



      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       Dict					):

             '''simple docstring'''







             import torch

             # support for torch, tf, jax etc.
             if hasattr(UpperCamelCase  ,		'''__array__'''					) and not isinstance(UpperCamelCase  ,		torch.Tensor					):
                    lowercase__      =					data_struct.__array__()
             # support for nested types like struct of list of struct
             if isinstance(UpperCamelCase  ,		np.ndarray					):
                    if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                           return self._consolidate([self.recursive_tensorize(UpperCamelCase					) for substruct in data_struct]					)
             elif isinstance(UpperCamelCase  ,		(list, tuple)					):
                    return self._consolidate([self.recursive_tensorize(UpperCamelCase					) for substruct in data_struct]					)
             return self._tensorize(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       dict					):

             '''simple docstring'''







             return map_nested(self._recursive_tensorize  ,		UpperCamelCase  ,		map_list=UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       pa.Table					):

             '''simple docstring'''







             lowercase__      =					self.numpy_arrow_extractor().extract_row(UpperCamelCase					)
             lowercase__      =					self.python_features_decoder.decode_row(UpperCamelCase					)
             return self.recursive_tensorize(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[Any]  ,		UpperCamelCase       :       pa.Table					):

             '''simple docstring'''







             lowercase__      =					self.numpy_arrow_extractor().extract_column(UpperCamelCase					)
             lowercase__      =					self.python_features_decoder.decode_column(UpperCamelCase  ,		pa_table.column_names[0]					)
             lowercase__      =					self.recursive_tensorize(UpperCamelCase					)
             lowercase__      =					self._consolidate(UpperCamelCase					)
             return column






      def  UpperCamelCase__						(self       :       Tuple  ,		UpperCamelCase       :       pa.Table					):

             '''simple docstring'''







             lowercase__      =					self.numpy_arrow_extractor().extract_batch(UpperCamelCase					)
             lowercase__      =					self.python_features_decoder.decode_batch(UpperCamelCase					)
             lowercase__      =					self.recursive_tensorize(UpperCamelCase					)
             for column_name in batch:
                    lowercase__      =					self._consolidate(batch[column_name]					)
             return batch




