






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


a		:       Dict			     = logging.getLogger(__name__)
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : Optional[Any]							       =	'token-classification'
      def __init__(      self					:			Optional[Any]		,      __lowercase					:			Optional[Any]					)							->      Any:
        if type(__lowercase					) == dict:
          __UpperCAmelCase							:	Optional[Any]           =  Namespace(**__lowercase					)
        __UpperCAmelCase							:	Tuple           =  import_module("""tasks"""					)
        try:
          __UpperCAmelCase							:	List[Any]           =  getattr(__lowercase		,      hparams.task_type					)
          __UpperCAmelCase							:	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__()}"""					)
        __UpperCAmelCase							:	Any           =  self.token_classification_task.get_labels(hparams.labels					)
        __UpperCAmelCase							:	Optional[int]           =  CrossEntropyLoss().ignore_index
        super().__init__(__lowercase		,      len(self.labels					)		,      self.mode					)
      def 	UpperCAmelCase      (      self					:			Tuple		,      **__lowercase					:			int					)							->      Optional[int]:
        return self.model(**__lowercase					)
      def 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			Optional[int]		,      __lowercase					:			Union[str, Any]					)							->      Union[str, Any]:
        __UpperCAmelCase							:	List[str]           =  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
        if self.config.model_type != "distilbert":
          __UpperCAmelCase							:	str           =  (
              batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
          )  # XLM and RoBERTa don"t use token_type_ids

        __UpperCAmelCase							:	Any           =  self(**__lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  outputs[0]
        # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
        return {"loss": loss}
      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      Tuple:
        __UpperCAmelCase							:	Optional[int]           =  self.hparams
        for mode in ["train", "dev", "test"]:
          __UpperCAmelCase							:	int           =  self._feature_file(__lowercase					)
          if os.path.exists(__lowercase					) and not args.overwrite_cache:
            logger.info("""Loading features from cached file %s"""		,      __lowercase					)
            __UpperCAmelCase							:	Any           =  torch.load(__lowercase					)
          else:
            logger.info("""Creating features from dataset file at %s"""		,      args.data_dir					)
            __UpperCAmelCase							:	Union[str, Any]           =  self.token_classification_task.read_examples_from_file(args.data_dir		,      __lowercase					)
            __UpperCAmelCase							:	Dict           =  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					:			Optional[int]		,      __lowercase					:			int		,      __lowercase					:			int		,      __lowercase					:			bool = False					)							->      DataLoader:
        __UpperCAmelCase							:	Optional[Any]           =  self._feature_file(__lowercase					)
        logger.info("""Loading features from cached file %s"""		,      __lowercase					)
        __UpperCAmelCase							:	Any           =  torch.load(__lowercase					)
        __UpperCAmelCase							:	List[Any]           =  torch.tensor([f.input_ids for f in features]		,      dtype=torch.long					)
        __UpperCAmelCase							:	str           =  torch.tensor([f.attention_mask for f in features]		,      dtype=torch.long					)
        if features[0].token_type_ids is not None:
          __UpperCAmelCase							:	Any           =  torch.tensor([f.token_type_ids for f in features]		,      dtype=torch.long					)
        else:
          __UpperCAmelCase							:	Union[str, Any]           =  torch.tensor([0 for f in features]		,      dtype=torch.long					)
          # HACK(we will not use this anymore soon)
        __UpperCAmelCase							:	Dict           =  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					:			Tuple		,      __lowercase					:			Tuple		,      __lowercase					:			Optional[Any]					)							->      int:
        """Compute validation""" ""
        __UpperCAmelCase							:	Tuple           =  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
        if self.config.model_type != "distilbert":
          __UpperCAmelCase							:	Optional[Any]           =  (
              batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
          )  # XLM and RoBERTa don"t use token_type_ids
        __UpperCAmelCase							:	List[str]           =  self(**__lowercase					)
        __UpperCAmelCase      , __UpperCAmelCase							:	Optional[Any]           =  outputs[:2]
        __UpperCAmelCase							:	str           =  logits.detach().cpu().numpy()
        __UpperCAmelCase							:	str           =  inputs["""labels"""].detach().cpu().numpy()
        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
      def 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			Tuple					)							->      Union[str, Any]:
        __UpperCAmelCase							:	Dict           =  torch.stack([x["""val_loss"""] for x in outputs]					).mean()
        __UpperCAmelCase							:	Optional[int]           =  np.concatenate([x["""pred"""] for x in outputs]		,      axis=0					)
        __UpperCAmelCase							:	int           =  np.argmax(__lowercase		,      axis=2					)
        __UpperCAmelCase							:	int           =  np.concatenate([x["""target"""] for x in outputs]		,      axis=0					)

        __UpperCAmelCase							:	int           =  dict(enumerate(self.labels					)					)
        __UpperCAmelCase							:	str           =  [[] for _ in range(out_label_ids.shape[0]					)]
        __UpperCAmelCase							:	Optional[int]           =  [[] 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							:	List[Any]           =  {
            """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					),
        }

        __UpperCAmelCase							:	List[str]           =  dict(results.items()					)
        __UpperCAmelCase							:	Optional[Any]           =  results
        return ret, preds_list, out_label_list
      def 	UpperCAmelCase      (      self					:			Union[str, Any]		,      __lowercase					:			List[str]					)							->      Optional[int]:
        # when stable
        __UpperCAmelCase      , __UpperCAmelCase      , __UpperCAmelCase							:	str           =  self._eval_end(__lowercase					)
        __UpperCAmelCase							:	Any           =  ret["""log"""]
        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
      def 	UpperCAmelCase      (      self					:			List[str]		,      __lowercase					:			str					)							->      Tuple:
        # updating to test_epoch_end instead of deprecated test_end
        __UpperCAmelCase      , __UpperCAmelCase      , __UpperCAmelCase							:	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
        __UpperCAmelCase							:	Optional[int]           =  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					:			Optional[int]		,      __lowercase					:			Any					)							->      Optional[Any]:
        # Add NER specific options
        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__":
       a		:       str			     = argparse.ArgumentParser()
       add_generic_args(parser, os.getcwd())
       a		:       Dict			     = NERTransformer.add_model_specific_args(parser, os.getcwd())
       a		:       List[Any]			     = parser.parse_args()
       a		:       Dict			     = NERTransformer(args)
       a		:       str			     = 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
              a		:       str			     = sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
              a		:       int			     = model.load_from_checkpoint(checkpoints[-1])
              trainer.test(model)










def 				lowerCamelCase__      (    __lowerCamelCase			:     Tuple  ,		__lowerCamelCase			:     Union[str, Any]		):
  __UpperCAmelCase							:	Tuple           =  [1]
  for i in range(2  ,		__lowerCamelCase		):
    factorials.append(factorials[-1] * i		)
  assert 0 <= k < factorials[-1] * n, "k out of bounds"

  __UpperCAmelCase							:	Optional[Any]           =  []
  __UpperCAmelCase							:	str           =  list(range(__lowerCamelCase		)		)

  # Find permutation
  while factorials:
    __UpperCAmelCase							:	Any           =  factorials.pop()
    __UpperCAmelCase      , __UpperCAmelCase							:	Optional[Any]           =  divmod(__lowerCamelCase  ,		__lowerCamelCase		)
    permutation.append(elements[number]		)
    elements.remove(elements[number]		)
  permutation.append(elements[0]		)

  return permutation


if __name__ == "__main__":
       import doctest

       doctest.testmod()


