



'''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


lowerCamelCase							:Tuple       = logging.getLogger(__name__)







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = 'token-classification'


	def __init__(self	,				lowercase							):
				if type(lowercase							) == dict:
							A_     :						Optional[int]     =					Namespace(**lowercase							)
				A_     :						Dict     =					import_module("""tasks"""							)
				try:
							A_     :						str     =					getattr(lowercase	,				hparams.task_type							)
							A_     :						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__()}'							)
				A_     :						Any     =					self.token_classification_task.get_labels(hparams.labels							)
				A_     :						str     =					CrossEntropyLoss().ignore_index
				super().__init__(lowercase	,				len(self.labels							)	,				self.mode							)


	def    _a     (self	,				**lowercase							):
				return self.model(**lowercase							)


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

				A_     :						List[Any]     =					self(**lowercase							)
				A_     :						Optional[int]     =					outputs[0]
				# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
				return {"loss": loss}


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


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


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


	def    _a     (self	,				lowercase							):
				A_     :						Union[str, Any]     =					torch.stack([x["""val_loss"""] for x in outputs]							).mean()
				A_     :						Any     =					np.concatenate([x["""pred"""] for x in outputs]	,				axis=0							)
				A_     :						Optional[int]     =					np.argmax(lowercase	,				axis=2							)
				A_     :						int     =					np.concatenate([x["""target"""] for x in outputs]	,				axis=0							)

				A_     :						Dict     =					dict(enumerate(self.labels							)							)
				A_     :						Tuple     =					[[] for _ in range(out_label_ids.shape[0]							)]
				A_     :						int     =					[[] 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]]							)

				A_     :						Tuple     =					{
				    """val_loss""": val_loss_mean,
				    """accuracy_score""": accuracy_score(lowercase	,				lowercase							),
				    """precision""": precision_score(lowercase	,				lowercase							),
				    """recall""": recall_score(lowercase	,				lowercase							),
				    """f1""": fa_score(lowercase	,				lowercase							),
				}

				A_     :						Optional[Any]     =					dict(results.items()							)
				A_     :						Optional[int]     =					results
				return ret, preds_list, out_label_list


	def    _a     (self	,				lowercase							):
				# when stable
				A_,   A_,   A_     :						Optional[Any]     =					self._eval_end(lowercase							)
				A_     :						str     =					ret["""log"""]
				return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


	def    _a     (self	,				lowercase							):
				# updating to test_epoch_end instead of deprecated test_end
				A_,   A_,   A_     :						List[str]     =					self._eval_end(lowercase							)

				# Converting to the dict required by pl
				# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
				# pytorch_lightning/trainer/logging.py#L139
				A_     :						Dict     =					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    _a     (lowercase	,				lowercase							):
				# Add NER specific options
				BaseTransformer.add_model_specific_args(lowercase	,				lowercase							)
				parser.add_argument(
				    """--task_type"""	,				default="""NER"""	,				type=lowercase	,				help="""Task type to fine tune in training (e.g. NER, POS, etc)"""							)
				parser.add_argument(
				    """--max_seq_length"""	,				default=128	,				type=lowercase	,				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=lowercase	,				help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""	,				)
				parser.add_argument(
				    """--gpus"""	,				default=0	,				type=lowercase	,				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__":
						lowerCamelCase							:Any       = argparse.ArgumentParser()
						add_generic_args(parser, os.getcwd())
						lowerCamelCase							:Optional[int]       = NERTransformer.add_model_specific_args(parser, os.getcwd())
						lowerCamelCase							:List[str]       = parser.parse_args()
						lowerCamelCase							:Union[str, Any]       = NERTransformer(args)
						lowerCamelCase							:Union[str, 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
												lowerCamelCase							:str       = sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
												lowerCamelCase							:List[str]       = model.load_from_checkpoint(checkpoints[-1])
												trainer.test(model)




'''simple docstring'''




def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return " ".join(input_str.split()[::-1]   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()