

'''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						    =      logging.getLogger(__name__)


class    A__ ( _snake_case     ):
    lowercase		   =						"token-classification"



    def __init__(   self						,			UpperCamelCase__    )  -> Any:





           '''simple docstring'''
           if type(UpperCamelCase__    ) == dict:
                  A_		   = Namespace(**UpperCamelCase__    )
           A_		   = import_module("""tasks"""    )
           try:
                  A_		   = getattr(UpperCamelCase__						,			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__(UpperCamelCase__						,			len(self.labels    )						,			self.mode    )



    def    snake_case_     (   self						,			**UpperCamelCase__    )  -> int:





           '''simple docstring'''
           return self.model(**UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__    )  -> List[Any]:





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



    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = self.hparams
           for mode in ["train", "dev", "test"]:
                  A_		   = self._feature_file(UpperCamelCase__    )
                  if os.path.exists(UpperCamelCase__    ) and not args.overwrite_cache:
                         logger.info("""Loading features from cached file %s"""						,			UpperCamelCase__    )
                         A_		   = torch.load(UpperCamelCase__    )
                  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						,			UpperCamelCase__    )
                         A_		   = self.token_classification_task.convert_examples_to_features(
                             UpperCamelCase__						,			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=UpperCamelCase__						,			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"""						,			UpperCamelCase__    )
                         torch.save(UpperCamelCase__						,			UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = False    )  -> DataLoader:





           '''simple docstring'''
           A_		   = self._feature_file(UpperCamelCase__    )
           logger.info("""Loading features from cached file %s"""						,			UpperCamelCase__    )
           A_		   = torch.load(UpperCamelCase__    )
           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(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )						,			batch_size=UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__    )  -> List[Any]:





           '''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(**UpperCamelCase__    )
           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    snake_case_     (   self						,			UpperCamelCase__    )  -> int:





           '''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(UpperCamelCase__						,			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(UpperCamelCase__						,			UpperCamelCase__    ),
               """precision""": precision_score(UpperCamelCase__						,			UpperCamelCase__    ),
               """recall""": recall_score(UpperCamelCase__						,			UpperCamelCase__    ),
               """f1""": fa_score(UpperCamelCase__						,			UpperCamelCase__    ),
           }

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



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> int:





           '''simple docstring'''
           # when stable
           A_   ,     A_   ,     A_		   = self._eval_end(UpperCamelCase__    )
           A_		   = ret["""log"""]
           return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> str:





           '''simple docstring'''
           # updating to test_epoch_end instead of deprecated test_end
           A_   ,     A_   ,     A_		   = self._eval_end(UpperCamelCase__    )

           # 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    snake_case_     (   UpperCamelCase__						,			UpperCamelCase__    )  -> List[Any]:





           '''simple docstring'''
           # Add NER specific options
           BaseTransformer.add_model_specific_args(UpperCamelCase__						,			UpperCamelCase__    )
           parser.add_argument(
               """--task_type"""						,			default="""NER"""						,			type=UpperCamelCase__						,			help="""Task type to fine tune in training (e.g. NER, POS, etc)"""    )
           parser.add_argument(
               """--max_seq_length"""						,			default=128						,			type=UpperCamelCase__						,			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=UpperCamelCase__						,			help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""						,			)
           parser.add_argument(
               """--gpus"""						,			default=0						,			type=UpperCamelCase__						,			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						    =      argparse.ArgumentParser()
      add_generic_args(parser, os.getcwd())
      __lowerCamelCase						    =      NERTransformer.add_model_specific_args(parser, os.getcwd())
      __lowerCamelCase						    =      parser.parse_args()
      __lowerCamelCase						    =      NERTransformer(args)
      __lowerCamelCase						    =      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						    =      sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
            __lowerCamelCase						    =      model.load_from_checkpoint(checkpoints[-1])
            trainer.test(model)



'''simple docstring'''







def  UpperCAmelCase__      ( UpperCAmelCase__ = 1_00			)     ->     int:
       A_		   = n * (n + 1) * (2 * n + 1) / 6
       A_		   = (n * (n + 1) / 2) ** 2
       return int(square_of_sum - sum_of_squares			)


if __name__ == "__main__":
      print(f"""{solution() = }""")
