

'''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			:				List[str] 			=					logging.getLogger(__name__)







class 							A__   (	A__  ):
  A__					=    'token-classification'





  def __init__(		self				:     Optional[Any]							, _a				:     Optional[Any]			)     ->   Tuple:





       '''simple docstring'''






       if type(_a			) == dict:
            _SCREAMING_SNAKE_CASE							   =Namespace(**_a			)
       _SCREAMING_SNAKE_CASE							   =import_module('tasks'			)
       try:
            _SCREAMING_SNAKE_CASE							   =getattr(_a							, hparams.task_type			)
            _SCREAMING_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__()}"			)
       _SCREAMING_SNAKE_CASE							   =self.token_classification_task.get_labels(hparams.labels			)
       _SCREAMING_SNAKE_CASE							   =CrossEntropyLoss().ignore_index
       super().__init__(_a							, len(self.labels			)							, self.mode			)





  def 						A  (		self				:     List[Any]							, **_a				:     Optional[Any]			)     ->   int:





       '''simple docstring'''






       return self.model(**_a			)





  def 						A  (		self				:     Any							, _a				:     Union[str, Any]							, _a				:     Any			)     ->   Tuple:





       '''simple docstring'''






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

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





  def 						A  (		self				:     List[str]			)     ->   List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.hparams
       for mode in ["train", "dev", "test"]:
            _SCREAMING_SNAKE_CASE							   =self._feature_file(_a			)
            if os.path.exists(_a			) and not args.overwrite_cache:
                 logger.info('Loading features from cached file %s'							, _a			)
                 _SCREAMING_SNAKE_CASE							   =torch.load(_a			)
            else:
                 logger.info('Creating features from dataset file at %s'							, args.data_dir			)
                 _SCREAMING_SNAKE_CASE							   =self.token_classification_task.read_examples_from_file(args.data_dir							, _a			)
                 _SCREAMING_SNAKE_CASE							   =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 						A  (		self				:     Optional[int]							, _a				:     int							, _a				:     int							, _a				:     bool = False			)     ->   DataLoader:





       '''simple docstring'''






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





  def 						A  (		self				:     str							, _a				:     List[Any]							, _a				:     Dict			)     ->   Dict:





       '''simple docstring'''






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





  def 						A  (		self				:     Optional[Any]							, _a				:     Tuple			)     ->   Optional[int]:





       '''simple docstring'''






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

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

       _SCREAMING_SNAKE_CASE							   ={
           '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			),
       }

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





  def 						A  (		self				:     List[Any]							, _a				:     Optional[Any]			)     ->   Tuple:





       '''simple docstring'''






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





  def 						A  (		self				:     List[str]							, _a				:     List[str]			)     ->   Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =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
       _SCREAMING_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 						A  (		_a				:     Tuple							, _a				:     Union[str, Any]			)     ->   Any:





       '''simple docstring'''






       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=128							, 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__":
     lowerCamelCase			:				Tuple 			=					argparse.ArgumentParser()
     add_generic_args(parser, os.getcwd())
     lowerCamelCase			:				Union[str, Any] 			=					NERTransformer.add_model_specific_args(parser, os.getcwd())
     lowerCamelCase			:				Union[str, Any] 			=					parser.parse_args()
     lowerCamelCase			:				str 			=					NERTransformer(args)
     lowerCamelCase			:				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
          lowerCamelCase			:				Dict 			=					sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
          lowerCamelCase			:				List[Any] 			=					model.load_from_checkpoint(checkpoints[-1])
          trainer.test(model)



'''simple docstring'''
from ..utils import DummyObject, requires_backends







class 							A__   (	metaclass=A__  ):
  A__					=    ['note_seq']





  def __init__(		self				:     List[str]							, *_a				:     Any							, **_a				:     Dict			)     ->   Optional[Any]:





       '''simple docstring'''






       requires_backends(self							, ['note_seq']			)





  @classmethod
  def 						A  (		cls				:     Any							, *_a				:     str							, **_a				:     List[Any]			)     ->   List[str]:





       '''simple docstring'''






       requires_backends(cls							, ['note_seq']			)





  @classmethod
  def 						A  (		cls				:     int							, *_a				:     Optional[Any]							, **_a				:     Optional[int]			)     ->   Tuple:





       '''simple docstring'''






       requires_backends(cls							, ['note_seq']			)
