

from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowercase_			=  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.
lowercase_			=  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.
lowercase_			=  0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     str				)							->				List[Any]:





    '''simple docstring'''






    A__   				=       len([g for position, g in enumerate(SCREAMING_SNAKE_CASE__				) if g == main_target[position]]				)
    return (item, float(SCREAMING_SNAKE_CASE__				))
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     str				)							->				Optional[Any]:





    '''simple docstring'''






    A__   				=       random.randint(0   ,      len(SCREAMING_SNAKE_CASE__				) - 1				)
    A__   				=       parent_a[:random_slice] + parent_a[random_slice:]
    A__   				=       parent_a[:random_slice] + parent_a[random_slice:]
    return (child_a, child_a)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     list[str]				)							->				Any:





    '''simple docstring'''






    A__   				=       list(SCREAMING_SNAKE_CASE__				)
    if random.uniform(0   ,      1				) < MUTATION_PROBABILITY:
        A__   				=       random.choice(SCREAMING_SNAKE_CASE__				)
    return "".join(SCREAMING_SNAKE_CASE__				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     tuple[str, float]   ,      SCREAMING_SNAKE_CASE__      :     list[tuple[str, float]]   ,      SCREAMING_SNAKE_CASE__      :     list[str]   ,      )							->				Tuple:





    '''simple docstring'''






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

        A__   				=       crossover(parent_a[0]   ,      SCREAMING_SNAKE_CASE__				)
        # Append new string to the population list.
        pop.append(mutate(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)				)
        pop.append(mutate(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)				)
    return pop
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     list[str]   ,      SCREAMING_SNAKE_CASE__      :     bool = True				)							->				Tuple:





    '''simple docstring'''






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

    # Generate random starting population.
    A__   				=       []
    for _ in range(SCREAMING_SNAKE_CASE__				):
        population.append(''.join([random.choice(SCREAMING_SNAKE_CASE__				) for i in range(len(SCREAMING_SNAKE_CASE__				)				)]				)				)

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

    # This loop will end when we find a perfect match for our target.
    while True:
        generation += 1
        total_population += len(SCREAMING_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__   				=       [evaluate(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				) for item in population]

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

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


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




import comet  # From: unbabel-comet
import torch

import datasets


lowercase_			=  datasets.logging.get_logger(__name__)

lowercase_			=  "\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",\n   author = \"Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon\",\n   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n   month = nov,\n   year = \"2020\",\n   address = \"Online\",\n   publisher = \"Association for Computational Linguistics\",\n   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",\n   pages = \"2685--2702\",\n}\n"

lowercase_			=  "\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n"

lowercase_			=  "\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric('comet')\n    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use\n    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]\n    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]\n    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [0.19, 0.92]\n"


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,							_KWARGS_DESCRIPTION			)
class 			A						(							datasets.Metric			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Optional[Any]		)->   str:





        '''simple docstring'''







        return datasets.MetricInfo(
            description=_DESCRIPTION,citation=_CITATION,homepage='https://unbabel.github.io/COMET/html/index.html',inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
                {
                    'sources': datasets.Value('string',id='sequence'		),
                    'predictions': datasets.Value('string',id='sequence'		),
                    'references': datasets.Value('string',id='sequence'		),
                }		),codebase_urls=['https://github.com/Unbabel/COMET'],reference_urls=[
                'https://github.com/Unbabel/COMET',
                'https://www.aclweb.org/anthology/2020.emnlp-main.213/',
                'http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6',
            ],)


    def 						snake_case__					(				self			:	Optional[Any],lowercase_			:	Dict		)->   str:





        '''simple docstring'''







        if self.config_name == "default":
            A__   				=       comet.load_from_checkpoint(comet.download_model('wmt20-comet-da'		)		)
        else:
            A__   				=       comet.load_from_checkpoint(comet.download_model(self.config_name		)		)


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	Dict,lowercase_			:	int,lowercase_			:	List[Any],lowercase_			:	Tuple=None,lowercase_			:	List[str]=False		)->   int:





        '''simple docstring'''







        if gpus is None:
            A__   				=       1 if torch.cuda.is_available() else 0
        A__   				=       {'src': sources, 'mt': predictions, 'ref': references}
        A__   				=       [dict(zip(lowercase_,lowercase_		)		) for t in zip(*data.values()		)]
        A__				,						A__   				=       self.scorer.predict(lowercase_,gpus=lowercase_,progress_bar=lowercase_		)
        return {"mean_score": mean_score, "scores": scores}

