

'''simple docstring'''
from __future__ import annotations

import random

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


def 	_lowerCAmelCase (						_UpperCamelCase						:					str							,			_UpperCamelCase						:					str    )				->      tuple[str, float]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =len([g for position, g in enumerate(_UpperCamelCase    ) if g == main_target[position]]    )
     return (item, float(_UpperCamelCase    ))


def 	_lowerCAmelCase (						_UpperCamelCase						:					str							,			_UpperCamelCase						:					str    )				->      tuple[str, str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =random.randint(0							,			len(_UpperCamelCase    ) - 1    )
     _SCREAMING_SNAKE_CASE							   =parent_a[:random_slice] + parent_a[random_slice:]
     _SCREAMING_SNAKE_CASE							   =parent_a[:random_slice] + parent_a[random_slice:]
     return (child_a, child_a)


def 	_lowerCAmelCase (						_UpperCamelCase						:					str							,			_UpperCamelCase						:					list[str]    )				->      str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =list(_UpperCamelCase    )
     if random.uniform(0							,			1    ) < MUTATION_PROBABILITY:
          _SCREAMING_SNAKE_CASE							   =random.choice(_UpperCamelCase    )
     return "".join(_UpperCamelCase    )


def 	_lowerCAmelCase (						_UpperCamelCase						:					tuple[str, float]							,			_UpperCamelCase						:					list[tuple[str, float]]							,			_UpperCamelCase						:					list[str]							,			)				->      list[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =[]
     # Generate more children proportionally to the fitness score.
     _SCREAMING_SNAKE_CASE							   =int(parent_a[1] * 1_00    ) + 1
     _SCREAMING_SNAKE_CASE							   =10 if child_n >= 10 else child_n
     for _ in range(_UpperCamelCase    ):
          _SCREAMING_SNAKE_CASE							   =population_score[random.randint(0							,			_UpperCamelCase    )][0]

          _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =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 	_lowerCAmelCase (						_UpperCamelCase						:					str							,			_UpperCamelCase						:					list[str]							,			_UpperCamelCase						:					bool = True    )				->      tuple[int, int, str]:
     """simple docstring"""




     if N_POPULATION < N_SELECTED:
          _SCREAMING_SNAKE_CASE							   =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.
     _SCREAMING_SNAKE_CASE							   =sorted({c for c in target if c not in genes}    )
     if not_in_genes_list:
          _SCREAMING_SNAKE_CASE							   =f"{not_in_genes_list} is not in genes list, evolution cannot converge"
          raise ValueError(_UpperCamelCase    )

     # Generate random starting population.
     _SCREAMING_SNAKE_CASE							   =[]
     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.
     _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =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.
          _SCREAMING_SNAKE_CASE							   =[evaluate(_UpperCamelCase							,			_UpperCamelCase    ) for item in population]

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



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase			:				List[str] 			=					{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Union[str, Any] 			=					[
         "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "IBertForMaskedLM",
         "IBertForMultipleChoice",
         "IBertForQuestionAnswering",
         "IBertForSequenceClassification",
         "IBertForTokenClassification",
         "IBertModel",
         "IBertPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ibert import (
              IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              IBertForMaskedLM,
              IBertForMultipleChoice,
              IBertForQuestionAnswering,
              IBertForSequenceClassification,
              IBertForTokenClassification,
              IBertModel,
              IBertPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase			:				str 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
