





'''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, 1_000))





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Tuple:







	'''simple docstring'''







	lowerCamelCase__              =	len([g for position, g in enumerate(_lowercase							) if g == main_target[position]]							)
	return (item, float(_lowercase							))





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Union[str, Any]:







	'''simple docstring'''







	lowerCamelCase__              =	random.randint(0 ,len(_lowercase							) - 1							)
	lowerCamelCase__              =	parent_a[:random_slice] + parent_a[random_slice:]
	lowerCamelCase__              =	parent_a[:random_slice] + parent_a[random_slice:]
	return (child_a, child_a)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Dict:







	'''simple docstring'''







	lowerCamelCase__              =	list(_lowercase							)
	if random.uniform(0 ,1							) < MUTATION_PROBABILITY:
		lowerCamelCase__              =	random.choice(_lowercase							)
	return "".join(_lowercase							)





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,)       ->   Any:







	'''simple docstring'''







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

		lowerCamelCase__              =	crossover(parent_a[0] ,_lowercase							)
		# Append new string to the population list.
		pop.append(mutate(_lowercase ,_lowercase							)							)
		pop.append(mutate(_lowercase ,_lowercase							)							)
	return pop





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case = True							)       ->   List[Any]:







	'''simple docstring'''







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

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

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

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

		# 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.
		lowerCamelCase__              =	[evaluate(_lowercase ,_lowercase							) for item in population]

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

		# This is selection
		for i in range(_lowercase							):
			population.extend(select(population_score[int(_lowercase							)] ,_lowercase ,_lowercase							)							)
			# 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(_lowercase							) > 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						=   basic(target_str, genes_list)
			print(
			    f"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
			)










import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


_a						=   logging.get_logger(__name__)






@dataclass
class       __A					:

			'''simple docstring'''







			lowerCAmelCase_							  =		field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()				)}				)
			lowerCAmelCase_							  =		field(
			    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}				)
			lowerCAmelCase_							  =		field(
			    default=128    ,       metadata={
			        """help""": (
			            """The maximum total input sequence length after tokenization. Sequences longer """
			            """than this will be truncated, sequences shorter will be padded."""
			        )
			    }    ,       )
			lowerCAmelCase_							  =		field(
			    default=lowerCAmelCase    ,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.task_name.lower()






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		"""train"""
			lowerCAmelCase_							  =		"""dev"""
			lowerCAmelCase_							  =		"""test"""






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		42
			lowerCAmelCase_							  =		42
			lowerCAmelCase_							  =		42





			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase = None ,						__lowerCAmelCase = Split.train ,						__lowerCAmelCase = None ,						):



				'''simple docstring'''







				warnings.warn(
				    '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
				    '''library. You can have a look at this example script for pointers: '''
				    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py''' ,						__lowerCAmelCase ,						)
				lowerCamelCase__              =	args
				lowerCamelCase__              =	glue_processors[args.task_name]()
				lowerCamelCase__              =	glue_output_modes[args.task_name]
				if isinstance(__lowerCAmelCase ,						__lowerCAmelCase							):
					try:
						lowerCamelCase__              =	Split[mode]
					except KeyError:
						raise KeyError('''mode is not a valid split name'''							)
        # Load data features from cache or dataset file
				lowerCamelCase__              =	os.path.join(
				    cache_dir if cache_dir is not None else args.data_dir ,						F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}' ,						)
				lowerCamelCase__              =	self.processor.get_labels()
				if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
				    "RobertaTokenizer",
				    "RobertaTokenizerFast",
				    "XLMRobertaTokenizer",
				    "BartTokenizer",
				    "BartTokenizerFast",
				):
					# HACK(label indices are swapped in RoBERTa pretrained model)
					lowerCamelCase__       ,			lowerCamelCase__              =	label_list[2], label_list[1]
				lowerCamelCase__              =	label_list

				# Make sure only the first process in distributed training processes the dataset,
				# and the others will use the cache.
				lowerCamelCase__              =	cached_features_file + '''.lock'''
				with FileLock(__lowerCAmelCase							):
					if os.path.exists(__lowerCAmelCase							) and not args.overwrite_cache:
						lowerCamelCase__              =	time.time()
						lowerCamelCase__              =	torch.load(__lowerCAmelCase							)
						logger.info(
						    F'Loading features from cached file {cached_features_file} [took %.3f s]' ,						time.time() - start							)
					else:
						logger.info(F'Creating features from dataset file at {args.data_dir}'							)

						if mode == Split.dev:
							lowerCamelCase__              =	self.processor.get_dev_examples(args.data_dir							)
						elif mode == Split.test:
							lowerCamelCase__              =	self.processor.get_test_examples(args.data_dir							)
						else:
							lowerCamelCase__              =	self.processor.get_train_examples(args.data_dir							)
						if limit_length is not None:
							lowerCamelCase__              =	examples[:limit_length]
						lowerCamelCase__              =	glue_convert_examples_to_features(
						    __lowerCAmelCase ,						__lowerCAmelCase ,						max_length=args.max_seq_length ,						label_list=__lowerCAmelCase ,						output_mode=self.output_mode ,						)
						lowerCamelCase__              =	time.time()
						torch.save(self.features ,						__lowerCAmelCase							)
						# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
						logger.info(
						    F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'							)





			def __len__(					self							):



				'''simple docstring'''







				return len(self.features							)





			def __getitem__(					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				return self.features[i]





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return self.label_list



