



'''simple docstring'''





from __future__ import annotations

import random

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




def        lowerCamelCase  (				_snake_case  :						str     ,_snake_case  :						str       ):





    '''simple docstring'''


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




def        lowerCamelCase  (				_snake_case  :						str     ,_snake_case  :						str       ):





    '''simple docstring'''


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




def        lowerCamelCase  (				_snake_case  :						str     ,_snake_case  :						list[str]       ):





    '''simple docstring'''


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




def        lowerCamelCase  (				_snake_case  :						tuple[str, float]     ,_snake_case  :						list[tuple[str, float]]     ,_snake_case  :						list[str]     ,):





    '''simple docstring'''


    lowercase__		       =						[]
    # Generate more children proportionally to the fitness score.
    lowercase__		       =						int(parent_a[1] * 100       ) + 1
    lowercase__		       =						10 if child_n >= 10 else child_n
    for _ in range(a_       ):
        lowercase__		       =						population_score[random.randint(0     ,a_       )][0]

        lowercase__		       =						crossover(parent_a[0]     ,a_       )
        # Append new string to the population list.
        pop.append(mutate(a_     ,a_       )       )
        pop.append(mutate(a_     ,a_       )       )
    return pop




def        lowerCamelCase  (				_snake_case  :						str     ,_snake_case  :						list[str]     ,_snake_case  :						bool = True       ):





    '''simple docstring'''


    if N_POPULATION < N_SELECTED:
        lowercase__		       =						f'''{N_POPULATION} must be bigger than {N_SELECTED}'''
        raise ValueError(a_       )
    # Verify that the target contains no genes besides the ones inside genes variable.
    lowercase__		       =						sorted({c for c in target if c not in genes}       )
    if not_in_genes_list:
        lowercase__		       =						f'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
        raise ValueError(a_       )

    # Generate random starting population.
    lowercase__		       =						[]
    for _ in range(a_       ):
        population.append("".join([random.choice(a_       ) for i in range(len(a_       )       )]       )       )

    # Just some logs to know what the algorithms is doing.
    lowercase__		       =						0, 0

    # This loop will end when we find a perfect match for our target.
    while True:
        generation += 1
        total_population += len(a_       )

        # 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__		       =						[evaluate(a_     ,a_       ) for item in population]

        # Check if there is a matching evolution.
        lowercase__		       =						sorted(a_     ,key=lambda _snake_case       : x[1]     ,reverse=a_       )
        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__		       =						population[: int(N_POPULATION / 3       )]
        population.clear()
        population.extend(a_       )
        # Normalize population score to be between 0 and 1.
        lowercase__		       =						[
            (item, score / len(a_       )) for item, score in population_score
        ]

        # This is selection
        for i in range(a_       ):
            population.extend(select(population_score[int(a_       )]     ,a_     ,a_       )       )
            # 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(a_       ) > N_POPULATION:
                break


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__												=     (
     "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
 )
 SCREAMING_SNAKE_CASE__												=     list(
     " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
     "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
 )
 SCREAMING_SNAKE_CASE__					,						SCREAMING_SNAKE_CASE__					,						SCREAMING_SNAKE_CASE__												=     basic(target_str, genes_list)
 print(
     f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
 )









'''simple docstring'''





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


class 				snake_case (unittest.TestCase		,      UpperCamelCase   ):



       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						load_tool("text-classification"      )
           self.tool.setup()
           lowercase__		       =						load_tool("text-classification"     ,remote=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						self.tool("That's quite cool"     ,["positive", "negative"]      )
           self.assertEqual(UpperCAmelCase_     ,"positive"      )



       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__		       =						self.remote_tool("That's quite cool"     ,["positive", "negative"]      )
           self.assertEqual(UpperCAmelCase_     ,"positive"      )



       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						self.tool(text="That's quite cool"     ,labels=["positive", "negative"]      )
           self.assertEqual(UpperCAmelCase_     ,"positive"      )







       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						self.remote_tool(text="That's quite cool"     ,labels=["positive", "negative"]      )
           self.assertEqual(UpperCAmelCase_     ,"positive"      )




