





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


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



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			"""token-classification"""






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






  def     lowercase		(    self			:		str ,  **lowerCAmelCase_			:		Any					)							->						Union[str, Any]:
     return self.model(**lowerCAmelCase_					)






  def     lowercase		(    self			:		str ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		Union[str, Any]					)							->						Tuple:
     __lowerCAmelCase							     =     {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
     if self.config.model_type != "distilbert":
        __lowerCAmelCase							     =     (
            batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
        )  # XLM and RoBERTa don"t use token_type_ids

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






  def     lowercase		(    self			:		Optional[int]					)							->						List[Any]:
     __lowerCAmelCase							     =     self.hparams
     for mode in ["train", "dev", "test"]:
        __lowerCAmelCase							     =     self._feature_file(lowerCAmelCase_					)
        if os.path.exists(lowerCAmelCase_					) and not args.overwrite_cache:
           logger.info('Loading features from cached file %s' ,  lowerCAmelCase_					)
           __lowerCAmelCase							     =     torch.load(lowerCAmelCase_					)
        else:
           logger.info('Creating features from dataset file at %s' ,  args.data_dir					)
           __lowerCAmelCase							     =     self.token_classification_task.read_examples_from_file(args.data_dir ,  lowerCAmelCase_					)
           __lowerCAmelCase							     =     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     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		bool = False					)							->						DataLoader:
     __lowerCAmelCase							     =     self._feature_file(lowerCAmelCase_					)
     logger.info('Loading features from cached file %s' ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     torch.load(lowerCAmelCase_					)
     __lowerCAmelCase							     =     torch.tensor([f.input_ids for f in features] ,  dtype=torch.long					)
     __lowerCAmelCase							     =     torch.tensor([f.attention_mask for f in features] ,  dtype=torch.long					)
     if features[0].token_type_ids is not None:
        __lowerCAmelCase							     =     torch.tensor([f.token_type_ids for f in features] ,  dtype=torch.long					)
     else:
        __lowerCAmelCase							     =     torch.tensor([0 for f in features] ,  dtype=torch.long					)
        # HACK(we will not use this anymore soon)
     __lowerCAmelCase							     =     torch.tensor([f.label_ids for f in features] ,  dtype=torch.long					)
     return DataLoader(
         TensorDataset(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					) ,  batch_size=lowerCAmelCase_					)






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






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Tuple					)							->						str:
     __lowerCAmelCase							     =     torch.stack([x['val_loss'] for x in outputs]					).mean()
     __lowerCAmelCase							     =     np.concatenate([x['pred'] for x in outputs] ,  axis=0					)
     __lowerCAmelCase							     =     np.argmax(lowerCAmelCase_ ,  axis=2					)
     __lowerCAmelCase							     =     np.concatenate([x['target'] for x in outputs] ,  axis=0					)

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

     __lowerCAmelCase							     =     {
         '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_					),
     }

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






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		Dict					)							->						Union[str, Any]:
     # when stable
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self._eval_end(lowerCAmelCase_					)
     __lowerCAmelCase							     =     ret['log']
     return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






  def     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		Optional[int]					)							->						Optional[Any]:
     # updating to test_epoch_end instead of deprecated test_end
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     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
     __lowerCAmelCase							     =     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		(    lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		Union[str, Any]					)							->						Optional[Any]:
     # Add NER specific options
     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__":
     _snake_case  :   List[Any]						   =  argparse.ArgumentParser()
     add_generic_args(parser, os.getcwd())
     _snake_case  :   Optional[Any]						   =  NERTransformer.add_model_specific_args(parser, os.getcwd())
     _snake_case  :   Dict						   =  parser.parse_args()
     _snake_case  :   Optional[Any]						   =  NERTransformer(args)
     _snake_case  :   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
          _snake_case  :   List[str]						   =  sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
          _snake_case  :   Union[str, Any]						   =  model.load_from_checkpoint(checkpoints[-1])
          trainer.test(model)








from functools import lru_cache

@lru_cache
def   a_   (			lowerCAmelCase_				:      int     ):
   if num < 0:
      raise ValueError('Number should not be negative.'     )

   return 1 if num in (0, 1) else num * factorial(num - 1     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

