

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      :					Dict					    =      logging.getLogger(__name__)





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    """token-classification"""

       def __init__(							self ,   lowercase__     ):







             '''simple docstring'''



             if type(lowercase__     ) == dict:
                   __A    =Namespace(**lowercase__     )
             __A    =import_module('''tasks'''     )
             try:
                   __A    =getattr(lowercase__ ,   hparams.task_type     )
                   __A    =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    =self.token_classification_task.get_labels(hparams.labels     )
             __A    =CrossEntropyLoss().ignore_index
             super().__init__(lowercase__ ,   len(self.labels     ) ,   self.mode     )

       def     __UpperCamelCase     (							self ,   **lowercase__     ):







             '''simple docstring'''



             return self.model(**lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



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

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.hparams
             for mode in ["train", "dev", "test"]:
                   __A    =self._feature_file(lowercase__     )
                   if os.path.exists(lowercase__     ) and not args.overwrite_cache:
                         logger.info('''Loading features from cached file %s''' ,   lowercase__     )
                         __A    =torch.load(lowercase__     )
                   else:
                         logger.info('''Creating features from dataset file at %s''' ,   args.data_dir     )
                         __A    =self.token_classification_task.read_examples_from_file(args.data_dir ,   lowercase__     )
                         __A    =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     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ = False     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



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

             __A    =dict(enumerate(self.labels     )     )
             __A    =[[] for _ in range(out_label_ids.shape[0]     )]
             __A    =[[] 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    ={
                 '''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    =dict(results.items()     )
             __A    =results
             return ret, preds_list, out_label_list

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    ,		__A    ,		__A    =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    =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     (							lowercase__ ,   lowercase__     ):







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





import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


_lowerCamelCase      :					List[Any]					    =      logging.getLogger(__name__)





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       def __init__(							self ,   lowercase__=-1     ):







             '''simple docstring'''



             __A    =label_idx

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             if isinstance(lowercase__ ,   lowercase__     ):
                   __A    =mode.value
             __A    =os.path.join(lowercase__ ,   f'''{mode}.txt'''     )
             __A    =1
             __A    =[]
             with open(lowercase__ ,   encoding='''utf-8'''     ) as f:
                   __A    =[]
                   __A    =[]
                   for line in f:
                         if line.startswith('''-DOCSTART-'''     ) or line == "" or line == "\n":
                               if words:
                                     examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,   words=lowercase__ ,   labels=lowercase__     )     )
                                     guid_index += 1
                                     __A    =[]
                                     __A    =[]
                         else:
                               __A    =line.split(''' '''     )
                               words.append(splits[0]     )
                               if len(lowercase__     ) > 1:
                                     labels.append(splits[self.label_idx].replace('''\n''' ,   ''''''     )     )
                               else:
                                     # Examples could have no label for mode = "test"
                                     labels.append('''O'''     )
                   if words:
                         examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,   words=lowercase__ ,   labels=lowercase__     )     )
             return examples

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =0
             for line in test_input_reader:
                   if line.startswith('''-DOCSTART-'''     ) or line == "" or line == "\n":
                         writer.write(lowercase__     )
                         if not preds_list[example_id]:
                               example_id += 1
                   elif preds_list[example_id]:
                         __A    =line.split()[0] + ''' ''' + preds_list[example_id].pop(0     ) + '''\n'''
                         writer.write(lowercase__     )
                   else:
                         logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.''' ,   line.split()[0]     )


       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             if path:
                   with open(lowercase__ ,   '''r'''     ) as f:
                         __A    =f.read().splitlines()
                   if "O" not in labels:
                         __A    =['''O'''] + labels
                   return labels
             else:
                   return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       def __init__(							self     ):







             '''simple docstring'''



             super().__init__(label_idx=-2     )


       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             if path:
                   with open(lowercase__ ,   '''r'''     ) as f:
                         __A    =f.read().splitlines()
                   if "O" not in labels:
                         __A    =['''O'''] + labels
                   return labels
             else:
                   return [
                       "O",
                       "B-ADVP",
                       "B-INTJ",
                       "B-LST",
                       "B-PRT",
                       "B-NP",
                       "B-SBAR",
                       "B-VP",
                       "B-ADJP",
                       "B-CONJP",
                       "B-PP",
                       "I-ADVP",
                       "I-INTJ",
                       "I-LST",
                       "I-PRT",
                       "I-NP",
                       "I-SBAR",
                       "I-VP",
                       "I-ADJP",
                       "I-CONJP",
                       "I-PP",
                   ]





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             if isinstance(lowercase__ ,   lowercase__     ):
                   __A    =mode.value
             __A    =os.path.join(lowercase__ ,   f'''{mode}.txt'''     )
             __A    =1
             __A    =[]

             with open(lowercase__ ,   encoding='''utf-8'''     ) as f:
                   for sentence in parse_incr(lowercase__     ):
                         __A    =[]
                         __A    =[]
                         for token in sentence:
                               words.append(token['''form''']     )
                               labels.append(token['''upos''']     )
                         assert len(lowercase__     ) == len(lowercase__     )
                         if words:
                               examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,   words=lowercase__ ,   labels=lowercase__     )     )
                               guid_index += 1
             return examples

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =0
             for sentence in parse_incr(lowercase__     ):
                   __A    =preds_list[example_id]
                   __A    =''''''
                   for token in sentence:
                         out += f'''{token['form']} ({token['upos']}|{s_p.pop(0     )}) '''
                   out += "\n"
                   writer.write(lowercase__     )
                   example_id += 1


       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             if path:
                   with open(lowercase__ ,   '''r'''     ) as f:
                         return f.read().splitlines()
             else:
                   return [
                       "ADJ",
                       "ADP",
                       "ADV",
                       "AUX",
                       "CCONJ",
                       "DET",
                       "INTJ",
                       "NOUN",
                       "NUM",
                       "PART",
                       "PRON",
                       "PROPN",
                       "PUNCT",
                       "SCONJ",
                       "SYM",
                       "VERB",
                       "X",
                   ]


