
'''simple docstring'''







import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


lowercase    :							Tuple      =					logging.getLogger(__name__)


class 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       """token-classification"""


							def __init__(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								if type(lowerCAmelCase_  ) == dict:
									_snake_case   						=		Namespace(**lowerCAmelCase_  )
								_snake_case   						=		import_module('tasks'  )
								try:
									_snake_case   						=		getattr(lowerCAmelCase_    ,   hparams.task_type  )
									_snake_case   						=		token_classification_task_clazz()
								except AttributeError:
									raise ValueError(
									    F'Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
									    F'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'  )
								_snake_case   						=		self.token_classification_task.get_labels(hparams.labels  )
								_snake_case   						=		CrossEntropyLoss().ignore_index
								super().__init__(lowerCAmelCase_    ,   len(self.labels  )    ,   self.mode  )


							def  lowerCamelCase				(       self    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								return self.model(**lowerCAmelCase_  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
								if self.config.model_type != "distilbert":
									_snake_case   						=		(
									    batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
									)  # XLM and RoBERTa don"t use token_type_ids

								_snake_case   						=		self(**lowerCAmelCase_  )
								_snake_case   						=		outputs[0]
								# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
								return {"loss": loss}


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.hparams
								for mode in ["train", "dev", "test"]:
									_snake_case   						=		self._feature_file(lowerCAmelCase_  )
									if os.path.exists(lowerCAmelCase_  ) and not args.overwrite_cache:
										logger.info('Loading features from cached file %s'    ,   lowerCAmelCase_  )
										_snake_case   						=		torch.load(lowerCAmelCase_  )
									else:
										logger.info('Creating features from dataset file at %s'    ,   args.data_dir  )
										_snake_case   						=		self.token_classification_task.read_examples_from_file(args.data_dir    ,   lowerCAmelCase_  )
										_snake_case   						=		self.token_classification_task.convert_examples_to_features(
										    lowerCAmelCase_    ,   self.labels    ,   args.max_seq_length    ,   self.tokenizer    ,   cls_token_at_end=bool(self.config.model_type in ['xlnet']  )    ,   cls_token=self.tokenizer.cls_token    ,   cls_token_segment_id=2 if self.config.model_type in ['xlnet'] else 0    ,   sep_token=self.tokenizer.sep_token    ,   sep_token_extra=lowerCAmelCase_    ,   pad_on_left=bool(self.config.model_type in ['xlnet']  )    ,   pad_token=self.tokenizer.pad_token_id    ,   pad_token_segment_id=self.tokenizer.pad_token_type_id    ,   pad_token_label_id=self.pad_token_label_id    ,   )
										logger.info('Saving features into cached file %s'    ,   lowerCAmelCase_  )
										torch.save(lowerCAmelCase_    ,   lowerCAmelCase_  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = False  ):





								"""simple docstring"""






								_snake_case   						=		self._feature_file(lowerCAmelCase_  )
								logger.info('Loading features from cached file %s'    ,   lowerCAmelCase_  )
								_snake_case   						=		torch.load(lowerCAmelCase_  )
								_snake_case   						=		torch.tensor([f.input_ids for f in features]    ,   dtype=torch.long  )
								_snake_case   						=		torch.tensor([f.attention_mask for f in features]    ,   dtype=torch.long  )
								if features[0].token_type_ids is not None:
									_snake_case   						=		torch.tensor([f.token_type_ids for f in features]    ,   dtype=torch.long  )
								else:
									_snake_case   						=		torch.tensor([0 for f in features]    ,   dtype=torch.long  )
									# HACK(we will not use this anymore soon)
								_snake_case   						=		torch.tensor([f.label_ids for f in features]    ,   dtype=torch.long  )
								return DataLoader(
								    TensorDataset(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  )    ,   batch_size=lowerCAmelCase_  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								"""Compute validation""" ""
								_snake_case   						=		{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
								if self.config.model_type != "distilbert":
									_snake_case   						=		(
									    batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
									)  # XLM and RoBERTa don"t use token_type_ids
								_snake_case   						=		self(**lowerCAmelCase_  )
								_snake_case						, _snake_case   						=		outputs[:2]
								_snake_case   						=		logits.detach().cpu().numpy()
								_snake_case   						=		inputs['labels'].detach().cpu().numpy()
								return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		torch.stack([x['val_loss'] for x in outputs]  ).mean()
								_snake_case   						=		np.concatenate([x['pred'] for x in outputs]    ,   axis=0  )
								_snake_case   						=		np.argmax(lowerCAmelCase_    ,   axis=2  )
								_snake_case   						=		np.concatenate([x['target'] for x in outputs]    ,   axis=0  )

								_snake_case   						=		dict(enumerate(self.labels  )  )
								_snake_case   						=		[[] for _ in range(out_label_ids.shape[0]  )]
								_snake_case   						=		[[] for _ in range(out_label_ids.shape[0]  )]

								for i in range(out_label_ids.shape[0]  ):
									for j in range(out_label_ids.shape[1]  ):
										if out_label_ids[i, j] != self.pad_token_label_id:
											out_label_list[i].append(label_map[out_label_ids[i][j]]  )
											preds_list[i].append(label_map[preds[i][j]]  )

								_snake_case   						=		{
								    'val_loss': val_loss_mean,
								    'accuracy_score': accuracy_score(lowerCAmelCase_    ,   lowerCAmelCase_  ),
								    'precision': precision_score(lowerCAmelCase_    ,   lowerCAmelCase_  ),
								    'recall': recall_score(lowerCAmelCase_    ,   lowerCAmelCase_  ),
								    'f1': fa_score(lowerCAmelCase_    ,   lowerCAmelCase_  ),
								}

								_snake_case   						=		dict(results.items()  )
								_snake_case   						=		results
								return ret, preds_list, out_label_list


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case						, _snake_case						, _snake_case   						=		self._eval_end(lowerCAmelCase_  )
								_snake_case   						=		ret['log']
								return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case						, _snake_case						, _snake_case   						=		self._eval_end(lowerCAmelCase_  )

								# Converting to the dict required by pl
								# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
								# pytorch_lightning/trainer/logging.py#L139
								_snake_case   						=		ret['log']
								# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
								return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




							@staticmethod
							def  lowerCamelCase				(       lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								BaseTransformer.add_model_specific_args(lowerCAmelCase_    ,   lowerCAmelCase_  )
								parser.add_argument(
								    '--task_type'    ,   default='NER'    ,   type=lowerCAmelCase_    ,   help='Task type to fine tune in training (e.g. NER, POS, etc)'  )
								parser.add_argument(
								    '--max_seq_length'    ,   default=1_28    ,   type=lowerCAmelCase_    ,   help=(
								        'The maximum total input sequence length after tokenization. Sequences longer '
								        'than this will be truncated, sequences shorter will be padded.'
								    )    ,   )

								parser.add_argument(
								    '--labels'    ,   default=''    ,   type=lowerCAmelCase_    ,   help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'    ,   )
								parser.add_argument(
								    '--gpus'    ,   default=0    ,   type=lowerCAmelCase_    ,   help='The number of GPUs allocated for this, it is by default 0 meaning none'    ,   )

								parser.add_argument(
								    '--overwrite_cache'    ,   action='store_true'    ,   help='Overwrite the cached training and evaluation sets'  )

								return parser


if __name__ == "__main__":
					lowercase    :							List[Any]      =					argparse.ArgumentParser()
					add_generic_args(parser, os.getcwd())
					lowercase    :							Union[str, Any]      =					NERTransformer.add_model_specific_args(parser, os.getcwd())
					lowercase    :							Dict      =					parser.parse_args()
					lowercase    :							List[Any]      =					NERTransformer(args)
					lowercase    :							List[Any]      =					generic_train(model, args)

					if args.do_predict:
										# See https://github.com/huggingface/transformers/issues/3159
										# pl use this default format to create a checkpoint:
										# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
										# /pytorch_lightning/callbacks/model_checkpoint.py#L322
										lowercase    :							int      =					sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
										lowercase    :							Dict      =					model.load_from_checkpoint(checkpoints[-1])
										trainer.test(model)



'''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}'''
					)

