"""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


UpperCAmelCase__  		=						logging.getLogger(__name__)


class        a				(		lowerCAmelCase_   ):
    _snake_case     :  Optional[int]     							=							'token-classification'


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


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				**__lowerCAmelCase	:  Tuple					):
      return self.model(**__lowerCAmelCase					)


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

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


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


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


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Tuple					):
      _UpperCAmelCase						 =      torch.stack([x["""val_loss"""] for x in outputs]					).mean()
      _UpperCAmelCase						 =      np.concatenate([x["""pred"""] for x in outputs]   ,				axis=0					)
      _UpperCAmelCase						 =      np.argmax(__lowerCAmelCase   ,				axis=2					)
      _UpperCAmelCase						 =      np.concatenate([x["""target"""] for x in outputs]   ,				axis=0					)

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

      _UpperCAmelCase						 =      {
          """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					),
      }

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


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  str					):
      # when stable
      _UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase						 =      self._eval_end(__lowerCAmelCase					)
      _UpperCAmelCase						 =      ret["""log"""]
      return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


    def      lowerCAmelCase_  (					self	:  Optional[int]   ,				__lowerCAmelCase	:  Dict					):
      # updating to test_epoch_end instead of deprecated test_end
      _UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase						 =      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
      _UpperCAmelCase						 =      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      lowerCAmelCase_  (					__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Dict					):
      # 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=128   ,				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__":
    UpperCAmelCase__  		=						argparse.ArgumentParser()
    add_generic_args(parser, os.getcwd())
    UpperCAmelCase__  		=						NERTransformer.add_model_specific_args(parser, os.getcwd())
    UpperCAmelCase__  		=						parser.parse_args()
    UpperCAmelCase__  		=						NERTransformer(args)
    UpperCAmelCase__  		=						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__  		=						sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
        UpperCAmelCase__  		=						model.load_from_checkpoint(checkpoints[-1])
        trainer.test(model)

"""simple docstring"""


from __future__ import annotations






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return [ord(lowercase		) - 96 for elem in plain]






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return "".join(chr(elem + 96		) for elem in encoded		)






def     __UpperCAmelCase					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      encode(input("""-> """		).strip().lower()		)
  print("""Encoded: """   ,lowercase		)
  print("""Decoded:"""   ,decode(lowercase		)		)


if __name__ == "__main__":
    main()
