





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


lowercase				:	str      						=  logging.getLogger(__name__)





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    Tuple	  =  '''token-classification'''

   def __init__( self ,  lowercase)      ->						List[Any]:







          '''simple docstring'''

          if type(lowercase) == dict:
                 a__						:     List[Any]			=    Namespace(**lowercase)
          a__						:     str			=    import_module('tasks')
          try:
                 a__						:     int			=    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__						:     List[str]			=    self.token_classification_task.get_labels(hparams.labels)
          a__						:     Union[str, Any]			=    CrossEntropyLoss().ignore_index
          super().__init__(lowercase ,  len(self.labels) ,  self.mode)

   def 							__lowercase		( self ,  **lowercase)      ->						Optional[Any]:







          '''simple docstring'''

          return self.model(**lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase)      ->						Optional[int]:







          '''simple docstring'''

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

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

   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          a__						:     Tuple			=    self.hparams
          for mode in ["train", "dev", "test"]:
                 a__						:     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__						:     str			=    torch.load(lowercase)
                 else:
                        logger.info('Creating features from dataset file at %s' ,  args.data_dir)
                        a__						:     Optional[int]			=    self.token_classification_task.read_examples_from_file(args.data_dir ,  lowercase)
                        a__						:     str			=    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 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase = False)      ->						DataLoader:







          '''simple docstring'''

          a__						:     int			=    self._feature_file(lowercase)
          logger.info('Loading features from cached file %s' ,  lowercase)
          a__						:     Any			=    torch.load(lowercase)
          a__						:     List[Any]			=    torch.tensor([f.input_ids for f in features] ,  dtype=torch.long)
          a__						:     str			=    torch.tensor([f.attention_mask for f in features] ,  dtype=torch.long)
          if features[0].token_type_ids is not None:
                 a__						:     List[Any]			=    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__						:     str			=    torch.tensor([f.label_ids for f in features] ,  dtype=torch.long)
          return DataLoader(
              TensorDataset(lowercase ,  lowercase ,  lowercase ,  lowercase) ,  batch_size=lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase)      ->						List[str]:







          '''simple docstring'''

          """Compute validation""" ""
          a__						:     int			=    {'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[str]			=    self(**lowercase)
          a__			,							a__						:     Tuple			=    outputs[:2]
          a__						:     Dict			=    logits.detach().cpu().numpy()
          a__						:     Dict			=    inputs['labels'].detach().cpu().numpy()
          return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

   def 							__lowercase		( self ,  lowercase)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     Dict			=    torch.stack([x['val_loss'] for x in outputs]).mean()
          a__						:     List[Any]			=    np.concatenate([x['pred'] for x in outputs] ,  axis=0)
          a__						:     str			=    np.argmax(lowercase ,  axis=2)
          a__						:     Optional[Any]			=    np.concatenate([x['target'] for x in outputs] ,  axis=0)

          a__						:     List[str]			=    dict(enumerate(self.labels))
          a__						:     Tuple			=    [[] for _ in range(out_label_ids.shape[0])]
          a__						:     Dict			=    [[] 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__						:     List[str]			=    {
              '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[Any]			=    results
          return ret, preds_list, out_label_list

   def 							__lowercase		( self ,  lowercase)      ->						Tuple:







          '''simple docstring'''

          a__			,							a__			,							a__						:     Any			=    self._eval_end(lowercase)
          a__						:     List[str]			=    ret['log']
          return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

   def 							__lowercase		( self ,  lowercase)      ->						Optional[Any]:







          '''simple docstring'''

          a__			,							a__			,							a__						:     Optional[Any]			=    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__						:     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 							__lowercase		( lowercase ,  lowercase)      ->						Tuple:







          '''simple docstring'''

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









def A_     (		A__    )  ->		int:

       if not isinstance(A__		,				A__    ):
              raise TypeError('only integers accepted as input'    )
       else:
              a__						:     List[Any]			=    str(abs(A__    )    )
              a__						:     Optional[int]			=    [list(A__    ) for char in range(len(A__    )    )]
              for index in range(len(A__    )    ):
                     num_transpositions[index].pop(A__    )
              return max(
                  int(''.join(list(A__    )    )    ) for transposition in num_transpositions    )


if __name__ == "__main__":
    __import__("""doctest""").testmod()


