
'''simple docstring'''







import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam


class 						__UpperCAmelCase      (					datasets.BeamBasedBuilder						):


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return datasets.DatasetInfo(
								    features=datasets.Features({'content': datasets.Value('string'  )}  )    ,   supervised_keys=lowerCAmelCase_    ,   )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,   gen_kwargs={'examples': get_test_dummy_examples()}  )]




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								import apache_beam as beam

								return pipeline | "Load Examples" >> beam.Create(lowerCAmelCase_  )


class 						__UpperCAmelCase      (					datasets.BeamBasedBuilder						):


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return datasets.DatasetInfo(
								    features=datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'  )}  )}  )    ,   supervised_keys=lowerCAmelCase_    ,   )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								return [
								    datasets.SplitGenerator(name=datasets.Split.TRAIN    ,   gen_kwargs={'examples': get_test_nested_examples()}  )
								]




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								import apache_beam as beam

								return pipeline | "Load Examples" >> beam.Create(lowerCAmelCase_  )







def 				SCREAMING_SNAKE_CASE__	(   )       ->	List[Any]:
	return [(i, {"content": content}) for i, content in enumerate(['foo', 'bar', 'foobar']	)]







def 				SCREAMING_SNAKE_CASE__	(   )       ->	Optional[Any]:
	return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['foo', 'bar', 'foobar']	)]





class 						__UpperCAmelCase      (					_lowerCamelCase						):


							@require_beam
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		len(get_test_dummy_examples()  )
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
									_snake_case   						=		DummyBeamDataset(cache_dir=lowerCAmelCase_    ,   beam_runner='DirectRunner'  )
									builder.download_and_prepare()
									self.assertTrue(
									    os.path.exists(
									        os.path.join(lowerCAmelCase_    ,   builder.name    ,   'default'    ,   '0.0.0'    ,   F'{builder.name}-train.arrow'  )  )  )
									self.assertDictEqual(builder.info.features    ,   datasets.Features({'content': datasets.Value('string'  )}  )  )
									_snake_case   						=		builder.as_dataset()
									self.assertEqual(dset['train'].num_rows    ,   lowerCAmelCase_  )
									self.assertEqual(dset['train'].info.splits['train'].num_examples    ,   lowerCAmelCase_  )
									self.assertDictEqual(dset['train'][0]    ,   get_test_dummy_examples()[0][1]  )
									self.assertDictEqual(
									    dset['train'][expected_num_examples - 1]    ,   get_test_dummy_examples()[expected_num_examples - 1][1]  )
									self.assertTrue(
									    os.path.exists(os.path.join(lowerCAmelCase_    ,   builder.name    ,   'default'    ,   '0.0.0'    ,   'dataset_info.json'  )  )  )
									del dset


							@require_beam
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								import apache_beam as beam

								_snake_case   						=		beam.io.parquetio.WriteToParquet

								_snake_case   						=		len(get_test_dummy_examples()  )
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
									_snake_case   						=		DummyBeamDataset(cache_dir=lowerCAmelCase_    ,   beam_runner='DirectRunner'  )
									with patch('apache_beam.io.parquetio.WriteToParquet'  ) as write_parquet_mock:
										_snake_case   						=		partial(lowerCAmelCase_    ,   num_shards=2  )
										builder.download_and_prepare()
									self.assertTrue(
									    os.path.exists(
									        os.path.join(
									            lowerCAmelCase_    ,   builder.name    ,   'default'    ,   '0.0.0'    ,   F'{builder.name}-train-00000-of-00002.arrow'  )  )  )
									self.assertTrue(
									    os.path.exists(
									        os.path.join(
									            lowerCAmelCase_    ,   builder.name    ,   'default'    ,   '0.0.0'    ,   F'{builder.name}-train-00000-of-00002.arrow'  )  )  )
									self.assertDictEqual(builder.info.features    ,   datasets.Features({'content': datasets.Value('string'  )}  )  )
									_snake_case   						=		builder.as_dataset()
									self.assertEqual(dset['train'].num_rows    ,   lowerCAmelCase_  )
									self.assertEqual(dset['train'].info.splits['train'].num_examples    ,   lowerCAmelCase_  )
									# Order is not preserved when sharding, so we just check that all the elements are there
									self.assertListEqual(sorted(dset['train']['content']  )    ,   sorted(['foo', 'bar', 'foobar']  )  )
									self.assertTrue(
									    os.path.exists(os.path.join(lowerCAmelCase_    ,   builder.name    ,   'default'    ,   '0.0.0'    ,   'dataset_info.json'  )  )  )
									del dset


							@require_beam
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								with tempfile.TemporaryDirectory() as tmp_cache_dir:
									_snake_case   						=		DummyBeamDataset(cache_dir=lowerCAmelCase_  )
									self.assertRaises(datasets.builder.MissingBeamOptions    ,   builder.download_and_prepare  )




							@require_beam
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		len(get_test_nested_examples()  )
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
									_snake_case   						=		NestedBeamDataset(cache_dir=lowerCAmelCase_    ,   beam_runner='DirectRunner'  )
									builder.download_and_prepare()
									self.assertTrue(
									    os.path.exists(
									        os.path.join(lowerCAmelCase_    ,   builder.name    ,   'default'    ,   '0.0.0'    ,   F'{builder.name}-train.arrow'  )  )  )
									self.assertDictEqual(
									    builder.info.features    ,   datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'  )}  )}  )  )
									_snake_case   						=		builder.as_dataset()
									self.assertEqual(dset['train'].num_rows    ,   lowerCAmelCase_  )
									self.assertEqual(dset['train'].info.splits['train'].num_examples    ,   lowerCAmelCase_  )
									self.assertDictEqual(dset['train'][0]    ,   get_test_nested_examples()[0][1]  )
									self.assertDictEqual(
									    dset['train'][expected_num_examples - 1]    ,   get_test_nested_examples()[expected_num_examples - 1][1]  )
									self.assertTrue(
									    os.path.exists(os.path.join(lowerCAmelCase_    ,   builder.name    ,   'default'    ,   '0.0.0'    ,   'dataset_info.json'  )  )  )
									del dset



'''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}'''
					)

