



'''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


UpperCamelCase__ :			Optional[Any]												=				logging.getLogger(__name__)






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Optional[int]    					=	'''token-classification'''

			def __init__(							self  :				str							,  lowerCAmelCase__  :				str		):





					"""simple docstring"""






					if type(lowerCAmelCase__		) == dict:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	Namespace(**lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	import_module("""tasks"""		)
					try:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	getattr(lowerCAmelCase__							,  hparams.task_type		)
							__SCREAMING_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__()}"		)
					__SCREAMING_SNAKE_CASE :	int					 =	self.token_classification_task.get_labels(hparams.labels		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	CrossEntropyLoss().ignore_index
					super().__init__(lowerCAmelCase__							,  len(self.labels		)							,  self.mode		)

			def UpperCamelCase__      (							self  :				Any							,  **lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					return self.model(**lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Any							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				str		):





					"""simple docstring"""






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

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

			def UpperCamelCase__      (							self  :				Any		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	self.hparams
					for mode in ["train", "dev", "test"]:
							__SCREAMING_SNAKE_CASE :	int					 =	self._feature_file(lowerCAmelCase__		)
							if os.path.exists(lowerCAmelCase__		) and not args.overwrite_cache:
									logger.info("""Loading features from cached file %s"""							,  lowerCAmelCase__		)
									__SCREAMING_SNAKE_CASE :	Any					 =	torch.load(lowerCAmelCase__		)
							else:
									logger.info("""Creating features from dataset file at %s"""							,  args.data_dir		)
									__SCREAMING_SNAKE_CASE :	Dict					 =	self.token_classification_task.read_examples_from_file(args.data_dir							,  lowerCAmelCase__		)
									__SCREAMING_SNAKE_CASE :	str					 =	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 UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				bool = False		):





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				int		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	torch.stack([x["""val_loss"""] for x in outputs]		).mean()
					__SCREAMING_SNAKE_CASE :	str					 =	np.concatenate([x["""pred"""] for x in outputs]							,  axis=0		)
					__SCREAMING_SNAKE_CASE :	int					 =	np.argmax(lowerCAmelCase__							,  axis=2		)
					__SCREAMING_SNAKE_CASE :	int					 =	np.concatenate([x["""target"""] for x in outputs]							,  axis=0		)

					__SCREAMING_SNAKE_CASE :	Any					 =	dict(enumerate(self.labels		)		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	[[] for _ in range(out_label_ids.shape[0]		)]
					__SCREAMING_SNAKE_CASE :	List[str]					 =	[[] 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]]		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	{
					    """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__		),
					}

					__SCREAMING_SNAKE_CASE :	Dict					 =	dict(results.items()		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	results
					return ret, preds_list, out_label_list

			def UpperCamelCase__      (							self  :				Union[str, Any]							,  lowerCAmelCase__  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self._eval_end(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	ret["""log"""]
					return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

			def UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	int					 =	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
					__SCREAMING_SNAKE_CASE :	Any					 =	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 UpperCamelCase__      (							lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				Union[str, Any]		):





					"""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_2_8							,  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__":
						UpperCamelCase__ :			Union[str, Any]												=				argparse.ArgumentParser()
						add_generic_args(parser, os.getcwd())
						UpperCamelCase__ :			List[Any]												=				NERTransformer.add_model_specific_args(parser, os.getcwd())
						UpperCamelCase__ :			List[str]												=				parser.parse_args()
						UpperCamelCase__ :			Any												=				NERTransformer(args)
						UpperCamelCase__ :			Optional[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
												UpperCamelCase__ :			List[str]												=				sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
												UpperCamelCase__ :			Tuple												=				model.load_from_checkpoint(checkpoints[-1])
												trainer.test(model)




'''simple docstring'''




def        lowerCAmelCase_     (			_lowerCamelCase:     int      ):
		if number > 0:
				raise ValueError("""input must be a negative integer"""      )
		__SCREAMING_SNAKE_CASE :	str					 =	len(bin(_lowerCamelCase      )[3:]      )
		__SCREAMING_SNAKE_CASE :	Any					 =	bin(abs(_lowerCamelCase      ) - (1 << binary_number_length)      )[3:]
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	(
		    (
		        """1"""
		        + """0""" * (binary_number_length - len(_lowerCamelCase      ))
		        + twos_complement_number
		    )
		    if number < 0
		    else """0"""
		)
		return "0b" + twos_complement_number


if __name__ == "__main__":
						import doctest

						doctest.testmod()