"""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


_a  :  int=				logging.getLogger(__name__)

class 	UpperCamelCase    (					__SCREAMING_SNAKE_CASE		):
				UpperCAmelCase       :   str					=	"""token-classification"""



				def __init__(self       :   Tuple			,						_A       :   int)					->			Union[str, Any]:




					if type(_a) == dict:
						__snake_case    :							str   	=				Namespace(**_a)
					__snake_case    :							Dict   	=				import_module('tasks')
					try:
						__snake_case    :							Optional[Any]   	=				getattr(_a			,						hparams.task_type)
						__snake_case    :							TokenClassificationTask   	=				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    :							Tuple   	=				self.token_classification_task.get_labels(hparams.labels)
					__snake_case    :							str   	=				CrossEntropyLoss().ignore_index
					super().__init__(_a			,						len(self.labels)			,						self.mode)



				def       _lowercase  (self       :   int			,						**_A       :   Any)					->			int:
					return self.model(**_a)



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   Union[str, Any]			,						_A       :   int)					->			Optional[Any]:
					__snake_case    :							str   	=				{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
					if self.config.model_type != "distilbert":
						__snake_case    :							str   	=				(
						    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
						)  # XLM and RoBERTa don"t use token_type_ids

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



				def       _lowercase  (self       :   Dict)					->			Optional[Any]:
					__snake_case    :							Union[str, Any]   	=				self.hparams
					for mode in ["train", "dev", "test"]:
						__snake_case    :							Union[str, Any]   	=				self._feature_file(_a)
						if os.path.exists(_a) and not args.overwrite_cache:
							logger.info('Loading features from cached file %s'			,						_a)
							__snake_case    :							Any   	=				torch.load(_a)
						else:
							logger.info('Creating features from dataset file at %s'			,						args.data_dir)
							__snake_case    :							List[str]   	=				self.token_classification_task.read_examples_from_file(args.data_dir			,						_a)
							__snake_case    :							Optional[Any]   	=				self.token_classification_task.convert_examples_to_features(
							    _a			,						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=_a			,						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'			,						_a)
							torch.save(_a			,						_a)



				def       _lowercase  (self       :   Dict			,						_A       :   int			,						_A       :   int			,						_A       :   bool = False)					->			str:
					__snake_case    :							int   	=				self._feature_file(_a)
					logger.info('Loading features from cached file %s'			,						_a)
					__snake_case    :							str   	=				torch.load(_a)
					__snake_case    :							Union[str, Any]   	=				torch.tensor([f.input_ids for f in features]			,						dtype=torch.long)
					__snake_case    :							List[Any]   	=				torch.tensor([f.attention_mask for f in features]			,						dtype=torch.long)
					if features[0].token_type_ids is not None:
						__snake_case    :							int   	=				torch.tensor([f.token_type_ids for f in features]			,						dtype=torch.long)
					else:
						__snake_case    :							Any   	=				torch.tensor([0 for f in features]			,						dtype=torch.long)
						# HACK(we will not use this anymore soon)
					__snake_case    :							List[Any]   	=				torch.tensor([f.label_ids for f in features]			,						dtype=torch.long)
					return DataLoader(
					    TensorDataset(_a			,						_a			,						_a			,						_a)			,						batch_size=_a)



				def       _lowercase  (self       :   Optional[int]			,						_A       :   List[str]			,						_A       :   Tuple)					->			Optional[Any]:
					"""Compute validation""" ""
					__snake_case    :							List[Any]   	=				{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
					if self.config.model_type != "distilbert":
						__snake_case    :							List[str]   	=				(
						    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
						)  # XLM and RoBERTa don"t use token_type_ids
					__snake_case    :							Union[str, Any]   	=				self(**_a)
					__snake_case    :							int   	=				outputs[:2]
					__snake_case    :							List[Any]   	=				logits.detach().cpu().numpy()
					__snake_case    :							int   	=				inputs["""labels"""].detach().cpu().numpy()
					return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   List[str])					->			Dict:
					__snake_case    :							Tuple   	=				torch.stack([x['val_loss'] for x in outputs]).mean()
					__snake_case    :							Optional[Any]   	=				np.concatenate([x['pred'] for x in outputs]			,						axis=0)
					__snake_case    :							List[str]   	=				np.argmax(_a			,						axis=2)
					__snake_case    :							Dict   	=				np.concatenate([x['target'] for x in outputs]			,						axis=0)

					__snake_case    :							Tuple   	=				dict(enumerate(self.labels))
					__snake_case    :							Optional[int]   	=				[[] for _ in range(out_label_ids.shape[0])]
					__snake_case    :							List[Any]   	=				[[] 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    :							Union[str, Any]   	=				{
					    """val_loss""": val_loss_mean,
					    """accuracy_score""": accuracy_score(_a			,						_a),
					    """precision""": precision_score(_a			,						_a),
					    """recall""": recall_score(_a			,						_a),
					    """f1""": fa_score(_a			,						_a),
					}

					__snake_case    :							Any   	=				dict(results.items())
					__snake_case    :							Any   	=				results
					return ret, preds_list, out_label_list



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   Any)					->			Union[str, Any]:
					__snake_case    :							Tuple   	=				self._eval_end(_a)
					__snake_case    :							int   	=				ret["""log"""]
					return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



				def       _lowercase  (self       :   Tuple			,						_A       :   List[Any])					->			List[str]:
					__snake_case    :							Tuple   	=				self._eval_end(_a)

					# Converting to the dict required by pl
					# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
					# pytorch_lightning/trainer/logging.py#L139
					__snake_case    :							str   	=				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       _lowercase  (_A       :   Tuple			,						_A       :   str)					->			Optional[Any]:
					BaseTransformer.add_model_specific_args(_a			,						_a)
					parser.add_argument(
					    '--task_type'			,						default='NER'			,						type=_a			,						help='Task type to fine tune in training (e.g. NER, POS, etc)')
					parser.add_argument(
					    '--max_seq_length'			,						default=1_28			,						type=_a			,						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=_a			,						help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'			,						)
					parser.add_argument(
					    '--gpus'			,						default=0			,						type=_a			,						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__":
	_a  :  str=				argparse.ArgumentParser()
	add_generic_args(parser, os.getcwd())
	_a  :  List[Any]=				NERTransformer.add_model_specific_args(parser, os.getcwd())
	_a  :  Dict=				parser.parse_args()
	_a  :  int=				NERTransformer(args)
	_a  :  List[str]=				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
		_a  :  Any=				sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
		_a  :  Any=				model.load_from_checkpoint(checkpoints[-1])
		trainer.test(model)






"""simple docstring"""







from __future__ import annotations

_a  :  str=				"#"

class 	UpperCamelCase    :



				def __init__(self       :   str)					->			None:
					__snake_case    :							dict   	=				{}



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   str)					->			None:
					__snake_case    :							Optional[Any]   	=				self._trie
					for char in text:
						if char not in trie:
							__snake_case    :							Optional[int]   	=				{}
						__snake_case    :							Optional[int]   	=				trie[char]
					__snake_case    :							Optional[int]   	=				True



				def       _lowercase  (self       :   List[str]			,						_A       :   str)					->			tuple | list:
					__snake_case    :							str   	=				self._trie
					for char in prefix:
						if char in trie:
							__snake_case    :							Optional[int]   	=				trie[char]
						else:
							return []
					return self._elements(_A)






				def       _lowercase  (self       :   List[Any]			,						_A       :   dict)					->			tuple:
					__snake_case    :							str   	=				[]
					for c, v in d.items():
						__snake_case    :							Optional[Any]   	=				[' '] if c == END else [(c + s) for s in self._elements(_A)]
						result.extend(_A)
					return tuple(_A)







_a  :  str=				Trie()
_a  :  Any=				("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
	trie.insert_word(word)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str						)							->    tuple:

	'''simple docstring'''





	__snake_case    :							Optional[int]   	=				trie.find_word(UpperCAmelCase_						)
	return tuple(string + word for word in suffixes						)
def 						__UpperCAmelCase       (					)							->    None:

	'''simple docstring'''





	print(autocomplete_using_trie('de'						)						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()





