





"""simple docstring"""



from __future__ import annotations

import random

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







def 		__lowercase     (   snake_case_  :  str				,snake_case_  :  str						)   ->tuple[str, float]:



    '''simple docstring'''







    __A    :						Optional[int]						 =     len([g for position, g in enumerate(snake_case_						) if g == main_target[position]]						)
    return (item, float(snake_case_						))







def 		__lowercase     (   snake_case_  :  str				,snake_case_  :  str						)   ->tuple[str, str]:



    '''simple docstring'''







    __A    :						List[str]						 =     random.randint(0				,len(snake_case_						) - 1						)
    __A    :						Any						 =     parent_a[:random_slice] + parent_a[random_slice:]
    __A    :						Optional[int]						 =     parent_a[:random_slice] + parent_a[random_slice:]
    return (child_a, child_a)







def 		__lowercase     (   snake_case_  :  str				,snake_case_  :  list[str]						)   ->str:



    '''simple docstring'''







    __A    :						List[Any]						 =     list(snake_case_						)
    if random.uniform(0				,1						) < MUTATION_PROBABILITY:
        __A    :						Optional[int]						 =     random.choice(snake_case_						)
    return "".join(snake_case_						)







def 		__lowercase     (   snake_case_  :  tuple[str, float]				,snake_case_  :  list[tuple[str, float]]				,snake_case_  :  list[str]				,)   ->list[str]:



    '''simple docstring'''







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

        __A ,       __A    :						Dict						 =     crossover(parent_a[0]				,snake_case_						)
        # Append new string to the population list.
        pop.append(mutate(snake_case_				,snake_case_						)						)
        pop.append(mutate(snake_case_				,snake_case_						)						)
    return pop







def 		__lowercase     (   snake_case_  :  str				,snake_case_  :  list[str]				,snake_case_  :  bool = True						)   ->tuple[int, int, str]:



    '''simple docstring'''







    if N_POPULATION < N_SELECTED:
        __A    :						List[Any]						 =     F"""{N_POPULATION} must be bigger than {N_SELECTED}"""
        raise ValueError(snake_case_						)
    # Verify that the target contains no genes besides the ones inside genes variable.
    __A    :						Optional[int]						 =     sorted({c for c in target if c not in genes}						)
    if not_in_genes_list:
        __A    :						Union[str, Any]						 =     F"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
        raise ValueError(snake_case_						)

    # Generate random starting population.
    __A    :						List[Any]						 =     []
    for _ in range(snake_case_						):
        population.append(''''''.join([random.choice(snake_case_						) for i in range(len(snake_case_						)						)]						)						)

    # Just some logs to know what the algorithms is doing.
    __A ,       __A    :						Union[str, Any]						 =     0, 0

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

        # 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.
        __A    :						Dict						 =     [evaluate(snake_case_				,snake_case_						) for item in population]

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

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


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











"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_ =			logging.get_logger(__name__)

a_ =			{
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """roberta"""

 def __init__(    self					,						__lowerCamelCase=5_0265					,						__lowerCamelCase=768					,						__lowerCamelCase=12					,						__lowerCamelCase=12					,						__lowerCamelCase=3072					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=512					,						__lowerCamelCase=2					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-1_2					,						__lowerCamelCase=1					,						__lowerCamelCase=0					,						__lowerCamelCase=2					,						__lowerCamelCase="absolute"					,						__lowerCamelCase=True					,						__lowerCamelCase=None					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(pad_token_id=__lowerCamelCase					,						bos_token_id=__lowerCamelCase					,						eos_token_id=__lowerCamelCase					,						**__lowerCamelCase  )

     __A    :						str						 =     vocab_size
     __A    :						Any						 =     hidden_size
     __A    :						Any						 =     num_hidden_layers
     __A    :						int						 =     num_attention_heads
     __A    :						List[Any]						 =     hidden_act
     __A    :						Any						 =     intermediate_size
     __A    :						List[str]						 =     hidden_dropout_prob
     __A    :						List[Any]						 =     attention_probs_dropout_prob
     __A    :						Optional[Any]						 =     max_position_embeddings
     __A    :						str						 =     type_vocab_size
     __A    :						int						 =     initializer_range
     __A    :						Tuple						 =     layer_norm_eps
     __A    :						str						 =     position_embedding_type
     __A    :						int						 =     use_cache
     __A    :						Optional[int]						 =     classifier_dropout






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.task == "multiple-choice":
         __A    :						List[Any]						 =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         __A    :						Optional[Any]						 =     {0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]  )




