




"""simple docstring"""




import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


lowerCAmelCase							:			Union[str, Any]     							=							"\\n@inproceedings{lin-2004-rouge,\n    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n    author = \"Lin, Chin-Yew\",\n    booktitle = \"Text Summarization Branches Out\",\n    month = jul,\n    year = \"2004\",\n    address = \"Barcelona, Spain\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W04-1013\",\n    pages = \"74--81\",\n}\n"

lowerCAmelCase							:			str     							=							"\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"

lowerCAmelCase							:			Any     							=							"\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric('rouge')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n    >>> print(results[\"rouge1\"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n"






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






				'''simple docstring'''





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return datasets.MetricInfo(
							    description=_DESCRIPTION		, citation=_CITATION		, inputs_description=_KWARGS_DESCRIPTION		, features=datasets.Features(
							        {
							            """predictions""": datasets.Value("""string"""		, id="""sequence""" ),
							            """references""": datasets.Value("""string"""		, id="""sequence""" ),
							        } )		, codebase_urls=["""https://github.com/google-research/google-research/tree/master/rouge"""]		, reference_urls=[
							        """https://en.wikipedia.org/wiki/ROUGE_(metric)""",
							        """https://github.com/google-research/google-research/tree/master/rouge""",
							    ]		, )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a=None		, _a=True		, _a=False ):





							"""simple docstring"""






							if rouge_types is None:
										lowerCamelCase			      =					["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

							lowerCamelCase			      =					rouge_scorer.RougeScorer(rouge_types=lowerCAmelCase_		, use_stemmer=lowerCAmelCase_ )
							if use_aggregator:
										lowerCamelCase			      =					scoring.BootstrapAggregator()
							else:
										lowerCamelCase			      =					[]

							for ref, pred in zip(lowerCAmelCase_		, lowerCAmelCase_ ):
										lowerCamelCase			      =					scorer.score(lowerCAmelCase_		, lowerCAmelCase_ )
										if use_aggregator:
													aggregator.add_scores(lowerCAmelCase_ )
										else:
													scores.append(lowerCAmelCase_ )

							if use_aggregator:
										lowerCamelCase			      =					aggregator.aggregate()
							else:
										lowerCamelCase			      =					{}
										for key in scores[0]:
													lowerCamelCase			      =					[score[key] for score in scores]

							return result





'''simple docstring'''







from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowercase    :							Dict      =					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    :							Optional[int]      =					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    :							Optional[Any]      =					0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))







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







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







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	str:
	_snake_case   						=		list(__A	)
	if random.uniform(0			,				1	) < MUTATION_PROBABILITY:
		_snake_case   						=		random.choice(__A	)
	return "".join(__A	)







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

		_snake_case						, _snake_case   						=		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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A = True	)       ->	tuple[int, int, str]:

	# Verify if N_POPULATION is bigger than N_SELECTED
	if N_POPULATION < N_SELECTED:
		_snake_case   						=		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.
	_snake_case   						=		sorted({c for c in target if c not in genes}	)
	if not_in_genes_list:
		_snake_case   						=		F'{not_in_genes_list} is not in genes list, evolution cannot converge'
		raise ValueError(__A	)

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

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

