





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


_UpperCAmelCase :    int             =							logging.getLogger(__name__)






class        snake_case_    (		__lowercase						):
  A_      							=   'token-classification'






  def __init__(							self			:					str       ,  _snake_case			:					str      )->Optional[Any]:






   '''simple docstring'''

   if type(_snake_case      ) == dict:
    __lowerCAmelCase					:	Dict      =       Namespace(**_snake_case      )
   __lowerCAmelCase					:	List[str]      =       import_module("""tasks"""      )
   try:
    __lowerCAmelCase					:	List[str]      =       getattr(_snake_case       ,  hparams.task_type      )
    __lowerCAmelCase					:	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__()}'''      )
   __lowerCAmelCase					:	Tuple      =       self.token_classification_task.get_labels(hparams.labels      )
   __lowerCAmelCase					:	List[str]      =       CrossEntropyLoss().ignore_index
   super().__init__(_snake_case       ,  len(self.labels      )       ,  self.mode      )






  def 				UpperCAmelCase__						(							self			:					List[Any]       ,  **_snake_case			:					Optional[Any]      )->Optional[int]:






   '''simple docstring'''

   return self.model(**_snake_case      )






  def 				UpperCAmelCase__						(							self			:					List[Any]       ,  _snake_case			:					Any       ,  _snake_case			:					Optional[int]      )->Tuple:






   '''simple docstring'''

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

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






  def 				UpperCAmelCase__						(							self			:					int      )->str:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					str       ,  _snake_case			:					int       ,  _snake_case			:					int       ,  _snake_case			:					bool = False      )->DataLoader:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					Optional[int]       ,  _snake_case			:					Tuple       ,  _snake_case			:					Union[str, Any]      )->int:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					Tuple       ,  _snake_case			:					Optional[int]      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       torch.stack([x["""val_loss"""] for x in outputs]      ).mean()
   __lowerCAmelCase					:	Optional[int]      =       np.concatenate([x["""pred"""] for x in outputs]       ,  axis=0      )
   __lowerCAmelCase					:	List[Any]      =       np.argmax(_snake_case       ,  axis=2      )
   __lowerCAmelCase					:	List[str]      =       np.concatenate([x["""target"""] for x in outputs]       ,  axis=0      )

   __lowerCAmelCase					:	Any      =       dict(enumerate(self.labels      )      )
   __lowerCAmelCase					:	int      =       [[] for _ in range(out_label_ids.shape[0]      )]
   __lowerCAmelCase					:	List[Any]      =       [[] 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					:	List[Any]      =       {
       """val_loss""": val_loss_mean,
       """accuracy_score""": accuracy_score(_snake_case       ,  _snake_case      ),
       """precision""": precision_score(_snake_case       ,  _snake_case      ),
       """recall""": recall_score(_snake_case       ,  _snake_case      ),
       """f1""": fa_score(_snake_case       ,  _snake_case      ),
   }

   __lowerCAmelCase					:	List[Any]      =       dict(results.items()      )
   __lowerCAmelCase					:	str      =       results
   return ret, preds_list, out_label_list






  def 				UpperCAmelCase__						(							self			:					str       ,  _snake_case			:					int      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       self._eval_end(_snake_case      )
   __lowerCAmelCase					:	Union[str, Any]      =       ret["""log"""]
   return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]       ,  _snake_case			:					Union[str, Any]      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       self._eval_end(_snake_case      )

   # Converting to the dict required by pl
   # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
   # pytorch_lightning/trainer/logging.py#L139
   __lowerCAmelCase					:	Tuple      =       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__						(							_snake_case			:					str       ,  _snake_case			:					str      )->Dict:






   '''simple docstring'''

   BaseTransformer.add_model_specific_args(_snake_case       ,  _snake_case      )
   parser.add_argument(
       """--task_type"""       ,  default="""NER"""       ,  type=_snake_case       ,  help="""Task type to fine tune in training (e.g. NER, POS, etc)"""      )
   parser.add_argument(
       """--max_seq_length"""       ,  default=128       ,  type=_snake_case       ,  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=_snake_case       ,  help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""       ,  )
   parser.add_argument(
       """--gpus"""       ,  default=0       ,  type=_snake_case       ,  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__":
      _UpperCAmelCase :    Optional[Any]             =							argparse.ArgumentParser()
      add_generic_args(parser, os.getcwd())
      _UpperCAmelCase :    str             =							NERTransformer.add_model_specific_args(parser, os.getcwd())
      _UpperCAmelCase :    Optional[int]             =							parser.parse_args()
      _UpperCAmelCase :    str             =							NERTransformer(args)
      _UpperCAmelCase :    Dict             =							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
            _UpperCAmelCase :    Union[str, Any]             =							sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
            _UpperCAmelCase :    Tuple             =							model.load_from_checkpoint(checkpoints[-1])
            trainer.test(model)






import random






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:Dict   )      ->   tuple:
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	List[str]      =       [], [], []
 for element in data:
  if element < pivot:
   less.append(SCREAMING_SNAKE_CASE   )
  elif element > pivot:
   greater.append(SCREAMING_SNAKE_CASE   )
  else:
   equal.append(SCREAMING_SNAKE_CASE   )
 return less, equal, greater







def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:int   )      ->   Dict:
 # index = len(items) // 2 when trying to find the median
 #   (value of index when items is sorted)

 # invalid input
 if index >= len(SCREAMING_SNAKE_CASE   ) or index < 0:
  return None

 __lowerCAmelCase					:	Union[str, Any]      =       items[random.randint(0   ,   len(SCREAMING_SNAKE_CASE   ) - 1   )]
 __lowerCAmelCase					:	int      =       0
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	Tuple      =       _partition(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       len(SCREAMING_SNAKE_CASE   )

 # index is the pivot
 if m <= index < m + count:
  return pivot
 # must be in smaller
 elif m > index:
  return quick_select(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 # must be in larger
 else:
  return quick_select(SCREAMING_SNAKE_CASE   ,   index - (m + count)   )