



'''simple docstring'''

import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
__lowerCAmelCase						      =       pytest.mark.integration


__lowerCAmelCase						      =       {'''comet'''}
__lowerCAmelCase						      =       importlib.util.find_spec('''fairseq''') is not None

__lowerCAmelCase						      =       {'''code_eval'''}
__lowerCAmelCase						      =       os.name == '''nt'''

__lowerCAmelCase						      =       {'''bertscore''', '''frugalscore''', '''perplexity'''}
__lowerCAmelCase						      =       importlib.util.find_spec('''transformers''') is not None



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Any:
		@wraps(lowerCAmelCase_  )
		def wrapper(self						,      lowerCAmelCase_  ):
				if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
						self.skipTest('"test requires Fairseq"'  )
				else:
						test_case(self						,      lowerCAmelCase_  )

		return wrapper



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Union[str, Any]:
		@wraps(lowerCAmelCase_  )
		def wrapper(self						,      lowerCAmelCase_  ):
				if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
						self.skipTest('"test requires transformers"'  )
				else:
						test_case(self						,      lowerCAmelCase_  )

		return wrapper



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				int:
		@wraps(lowerCAmelCase_  )
		def wrapper(self						,      lowerCAmelCase_  ):
				if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
						self.skipTest('"test not supported on Windows"'  )
				else:
						test_case(self						,      lowerCAmelCase_  )

		return wrapper



def 					__lowerCamelCase					(				)      ->				Tuple:
		_a					:				Optional[int]				=							[metric_dir.split(os.sep  )[-2] for metric_dir in glob.glob('./metrics/*/'  )]
		return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished

@parameterized.named_parameters(get_local_metric_names()       )
@for_all_test_methods(
    _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase       )
@local
class      __magic_name__						(   parameterized.TestCase       ):
							lowerCAmelCase	:   List[str]           = {}
							lowerCAmelCase	:   Optional[int]           = None







							@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'		)
							@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'		)
							def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	Optional[Any]		):
									_a					:				Tuple				=							'[...]'
									_a					:				Dict				=							importlib.import_module(
									    datasets.load.metric_module_factory(os.path.join('metrics'     ,_UpperCAmelCase		)		).module_path		)
									_a					:				Optional[int]				=							datasets.load.import_main_class(metric_module.__name__     ,dataset=_UpperCAmelCase		)
									# check parameters
									_a					:				Optional[int]				=							inspect.signature(metric._compute		).parameters
									self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()		)		)  # no **kwargs
									# run doctest
									with self.patch_intensive_calls(_UpperCAmelCase     ,metric_module.__name__		):
											with self.use_local_metrics():
													try:
															_a					:				Optional[int]				=							doctest.testmod(_UpperCAmelCase     ,verbose=_UpperCAmelCase     ,raise_on_error=_UpperCAmelCase		)
													except doctest.UnexpectedException as e:
															raise e.exc_info[1]  # raise the exception that doctest caught
									self.assertEqual(results.failed     ,0		)
									self.assertGreater(results.attempted     ,1		)







							@slow
							def 							__lowercase      (							self :	Tuple     ,_UpperCAmelCase :	Dict		):
									_a					:				Tuple				=							'[...]'
									_a					:				Optional[Any]				=							importlib.import_module(
									    datasets.load.metric_module_factory(os.path.join('metrics'     ,_UpperCAmelCase		)		).module_path		)
									# run doctest
									with self.use_local_metrics():
											_a					:				int				=							doctest.testmod(_UpperCAmelCase     ,verbose=_UpperCAmelCase     ,raise_on_error=_UpperCAmelCase		)
									self.assertEqual(results.failed     ,0		)
									self.assertGreater(results.attempted     ,1		)







							@contextmanager
							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	List[str]		):
									if metric_name in self.INTENSIVE_CALLS_PATCHER:
											with self.INTENSIVE_CALLS_PATCHER[metric_name](_UpperCAmelCase		):
													yield
									else:
											yield







							@contextmanager
							def 							__lowercase      (							self :	Optional[int]		):
									def load_local_metric(_UpperCAmelCase :	Tuple     ,*_UpperCAmelCase :	Dict     ,**_UpperCAmelCase :	Tuple		):
											return load_metric(os.path.join('metrics'     ,_UpperCAmelCase		)     ,*_UpperCAmelCase     ,**_UpperCAmelCase		)

									with patch('datasets.load_metric'		) as mock_load_metric:
											_a					:				Any				=							load_local_metric
											yield







							@classmethod
							def 							__lowercase      (							cls :	str     ,_UpperCAmelCase :	List[str]		):
									def wrapper(_UpperCAmelCase :	int		):
											_a					:				Optional[Any]				=							contextmanager(_UpperCAmelCase		)
											_a					:				Optional[int]				=							patcher
											return patcher

									return wrapper



@LocalMetricTest.register_intensive_calls_patcher('bleurt'  )
def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				List[str]:
		import tensorflow.compat.va as tf
		from bleurt.score import Predictor

		tf.flags.DEFINE_string('sv'						,      ''						,      ''  )  # handle pytest cli flags

		class      __magic_name__						(   _UpperCamelCase       ):







									def 							__lowercase      (							self :	int     ,_UpperCAmelCase :	Union[str, Any]		):
											assert len(input_dict['input_ids']		) == 2
											return np.array([1.03, 1.04]		)

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
		with patch('bleurt.score._create_predictor'  ) as mock_create_predictor:
				_a					:				int				=							MockedPredictor()
				yield



@LocalMetricTest.register_intensive_calls_patcher('bertscore'  )
def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Union[str, Any]:
		import torch

		def bert_cos_score_idf(lowerCAmelCase_						,      lowerCAmelCase_						,      *lowerCAmelCase_						,      **lowerCAmelCase_  ):
				return torch.tensor([[1.0, 1.0, 1.0]] * len(lowerCAmelCase_  )  )

		# mock get_model which is supposed to do download a bert model
		# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
		with patch('bert_score.scorer.get_model'  ), patch(
		    'bert_score.scorer.bert_cos_score_idf'  ) as mock_bert_cos_score_idf:
				_a					:				Any				=							bert_cos_score_idf
				yield



@LocalMetricTest.register_intensive_calls_patcher('comet'  )
def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Dict:
		def load_from_checkpoint(lowerCAmelCase_  ):

				class      __magic_name__						:







											def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Dict     ,*_UpperCAmelCase :	int     ,**_UpperCAmelCase :	str		):
													assert len(_UpperCAmelCase		) == 2
													_a					:				Dict				=							[0.19, 0.92]
													return scores, sum(_UpperCAmelCase		) / len(_UpperCAmelCase		)
				return Model()

		# mock load_from_checkpoint which is supposed to do download a bert model
		# mock load_from_checkpoint which is supposed to do download a bert model
		with patch('comet.download_model'  ) as mock_download_model:
				_a					:				Any				=							None
				with patch('comet.load_from_checkpoint'  ) as mock_load_from_checkpoint:
						_a					:				Optional[Any]				=							load_from_checkpoint
						yield



def 					__lowerCamelCase					(				)      ->				Tuple:
		_a					:				Dict				=							load_metric(os.path.join('metrics'						,      'seqeval'  )  )
		_a					:				Optional[int]				=							'ERROR'
		_a					:				Optional[Any]				=							f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
		with pytest.raises(lowerCAmelCase_						,      match=re.escape(lowerCAmelCase_  )  ):
				metric.compute(predictions=[]						,      references=[]						,      scheme=lowerCAmelCase_  )









'''simple docstring'''

from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__lowerCAmelCase						      =       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.
__lowerCAmelCase						      =       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.
__lowerCAmelCase						      =       0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				tuple[str, float]:
		_a					:				List[Any]				=							len([g for position, g in enumerate(lowerCAmelCase_  ) if g == main_target[position]]  )
		return (item, float(lowerCAmelCase_  ))



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				tuple[str, str]:
		_a					:				Dict				=							random.randint(0						,      len(lowerCAmelCase_  ) - 1  )
		_a					:				Optional[int]				=							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 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				str:
		_a					:				Optional[Any]				=							list(lowerCAmelCase_  )
		if random.uniform(0						,      1  ) < MUTATION_PROBABILITY:
				_a					:				Optional[int]				=							random.choice(lowerCAmelCase_  )
		return "".join(lowerCAmelCase_  )



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

				_a	, _a					:				Tuple				=							crossover(parent_a[0]						,      lowerCAmelCase_  )
				# Append new string to the population list.
				pop.append(mutate(lowerCAmelCase_						,      lowerCAmelCase_  )  )
				pop.append(mutate(lowerCAmelCase_						,      lowerCAmelCase_  )  )
		return pop



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_ = True  )      ->				tuple[int, int, str]:

		# Verify if N_POPULATION is bigger than N_SELECTED
		if N_POPULATION < N_SELECTED:
				_a					:				Dict				=							f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
				raise ValueError(lowerCAmelCase_  )
		# 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					:				List[Any]				=							f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
				raise ValueError(lowerCAmelCase_  )

		# Generate random starting population.
		_a					:				Union[str, Any]				=							[]
		for _ in range(lowerCAmelCase_  ):
				population.append(''.join([random.choice(lowerCAmelCase_  ) for i in range(len(lowerCAmelCase_  )  )]  )  )

		# 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(lowerCAmelCase_  )

				# 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					:				Optional[Any]				=							[evaluate(lowerCAmelCase_						,      lowerCAmelCase_  ) for item in population]

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

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


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




