

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  :Union[str, Any]										=       logging.getLogger(__name__)

class    _a (       SCREAMING_SNAKE_CASE__							):




      """simple docstring"""

      _lowerCamelCase    :       Optional[Any]							=    '''token-classification'''





      def __init__(					self				:							str		,       UpperCAmelCase				:							List[Any]     ):
            if type(A__     ) == dict:
                  A_		   =							Namespace(**A__     )
            A_		   =							import_module("tasks"     )
            try:
                  A_		   =							getattr(A__		,       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__(A__		,       len(self.labels     )		,       self.mode     )





      def 						__A     (					self				:							List[str]		,       **UpperCAmelCase				:							List[str]     ):
            return self.model(**A__     )





      def 						__A     (					self				:							List[str]		,       UpperCAmelCase				:							Tuple		,       UpperCAmelCase				:							Optional[Any]     ):
            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(**A__     )
            A_		   =							outputs[0]
            # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
            return {"loss": loss}





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





      def 						__A     (					self				:							int		,       UpperCAmelCase				:							Any		,       UpperCAmelCase				:							Optional[Any]		,       UpperCAmelCase				:							int = False     ):
            A_		   =							self._feature_file(A__     )
            logger.info("Loading features from cached file %s"		,       A__     )
            A_		   =							torch.load(A__     )
            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(A__		,       A__		,       A__		,       A__     )		,       batch_size=A__     )





      def 						__A     (					self				:							Union[str, Any]		,       UpperCAmelCase				:							Union[str, Any]		,       UpperCAmelCase				:							Any     ):
            """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(**A__     )
            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 						__A     (					self				:							Dict		,       UpperCAmelCase				:							int     ):
            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(A__		,       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(A__		,       A__     ),
                "precision": precision_score(A__		,       A__     ),
                "recall": recall_score(A__		,       A__     ),
                "f1": fa_score(A__		,       A__     ),
            }

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





      def 						__A     (					self				:							Tuple		,       UpperCAmelCase				:							Union[str, Any]     ):
            # when stable
            A_   ,			A_   ,			A_		   =							self._eval_end(A__     )
            A_		   =							ret["log"]
            return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





      def 						__A     (					self				:							str		,       UpperCAmelCase				:							Optional[int]     ):
            # updating to test_epoch_end instead of deprecated test_end
            A_   ,			A_   ,			A_		   =							self._eval_end(A__     )

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

from functools import lru_cache



@lru_cache
def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					if num < 0:
										raise ValueError('''Number should not be negative.'''		)

					return 1 if num in (0, 1) else num * factorial(num - 1		)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




