






'''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


__A							:							Union[str, Any]  							=		logging.getLogger(__name__)
class 				lowercase       (							A_      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							'''token-classification'''

    def __init__(					self     :     int				,     __lowerCamelCase     :     List[str]     )							->							Optional[Any]:

     '''simple docstring'''




     if type(__lowerCamelCase     ) == dict:
      lowerCamelCase__  =     Namespace(**__lowerCamelCase     )
     lowerCamelCase__  =     import_module("tasks"     )
     try:
      lowerCamelCase__  =     getattr(__lowerCamelCase				,     hparams.task_type     )
      lowerCamelCase__  =     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__  =     self.token_classification_task.get_labels(hparams.labels     )
     lowerCamelCase__  =     CrossEntropyLoss().ignore_index
     super().__init__(__lowerCamelCase				,     len(self.labels     )				,     self.mode     )

    def    a__				(					self     :     Tuple				,     **__lowerCamelCase     :     int     )							->							List[str]:

     '''simple docstring'''




     return self.model(**__lowerCamelCase     )

    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     Union[str, Any]     )							->							Optional[Any]:

     '''simple docstring'''




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

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

    def    a__				(					self     :     int     )							->							List[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     self.hparams
     for mode in ["train", "dev", "test"]:
      lowerCamelCase__  =     self._feature_file(__lowerCamelCase     )
      if os.path.exists(__lowerCamelCase     ) and not args.overwrite_cache:
       logger.info("Loading features from cached file %s"				,     __lowerCamelCase     )
       lowerCamelCase__  =     torch.load(__lowerCamelCase     )
      else:
       logger.info("Creating features from dataset file at %s"				,     args.data_dir     )
       lowerCamelCase__  =     self.token_classification_task.read_examples_from_file(args.data_dir				,     __lowerCamelCase     )
       lowerCamelCase__  =     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    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     Optional[Any] = False     )							->							Any:

     '''simple docstring'''




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

    def    a__				(					self     :     int				,     __lowerCamelCase     :     List[Any]				,     __lowerCamelCase     :     List[Any]     )							->							str:

     '''simple docstring'''




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

    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     int     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     torch.stack([x["val_loss"] for x in outputs]     ).mean()
     lowerCamelCase__  =     np.concatenate([x["pred"] for x in outputs]				,     axis=0     )
     lowerCamelCase__  =     np.argmax(__lowerCamelCase				,     axis=2     )
     lowerCamelCase__  =     np.concatenate([x["target"] for x in outputs]				,     axis=0     )

     lowerCamelCase__  =     dict(enumerate(self.labels     )     )
     lowerCamelCase__  =     [[] for _ in range(out_label_ids.shape[0]     )]
     lowerCamelCase__  =     [[] 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__  =     {
         """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     ),
     }

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

    def    a__				(					self     :     str				,     __lowerCamelCase     :     Any     )							->							int:

     '''simple docstring'''




     lowerCamelCase__  =     self._eval_end(__lowerCamelCase     )
     lowerCamelCase__  =     ret["""log"""]
     return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

    def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     List[str]     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     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
     lowerCamelCase__  =     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__				(					__lowerCamelCase     :     Any				,     __lowerCamelCase     :     Union[str, Any]     )							->							Any:

     '''simple docstring'''




     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__":
  __A							:							List[Any]  							=		argparse.ArgumentParser()
  add_generic_args(parser, os.getcwd())
  __A							:							int  							=		NERTransformer.add_model_specific_args(parser, os.getcwd())
  __A							:							Tuple  							=		parser.parse_args()
  __A							:							Any  							=		NERTransformer(args)
  __A							:							Union[str, Any]  							=		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							:							Optional[int]  							=		sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
    __A							:							List[Any]  							=		model.load_from_checkpoint(checkpoints[-1])
    trainer.test(model)









'''simple docstring'''





import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
 import torch
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"char"
    lowerCAmelCase__      				=							"bpe"
    lowerCAmelCase__      				=							"wp"
__A							:							Any  							=		(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["image_processor", "char_tokenizer"]
    lowerCAmelCase__      				=							"ViTImageProcessor"
    lowerCAmelCase__      				=							"MgpstrTokenizer"

    def __init__(					self     :     Any				,     __lowerCamelCase     :     Optional[Any]=None				,     __lowerCamelCase     :     List[str]=None				,     **__lowerCamelCase     :     Any     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     None
     if "feature_extractor" in kwargs:
      warnings.warn(
          "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
          " instead."				,     __lowerCamelCase				,     )
      lowerCamelCase__  =     kwargs.pop("feature_extractor"     )

     lowerCamelCase__  =     image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
      raise ValueError("You need to specify an `image_processor`."     )
     if tokenizer is None:
      raise ValueError("You need to specify a `tokenizer`."     )

     lowerCamelCase__  =     tokenizer
     lowerCamelCase__  =     AutoTokenizer.from_pretrained("gpt2"     )
     lowerCamelCase__  =     AutoTokenizer.from_pretrained("bert-base-uncased"     )

     super().__init__(__lowerCamelCase				,     __lowerCamelCase     )

    def __call__(					self     :     List[str]				,     __lowerCamelCase     :     Union[str, Any]=None				,     __lowerCamelCase     :     List[Any]=None				,     __lowerCamelCase     :     str=None				,     **__lowerCamelCase     :     Optional[Any]     )							->							str:

     '''simple docstring'''




     if images is None and text is None:
      raise ValueError("You need to specify either an `images` or `text` input to process."     )

     if images is not None:
      lowerCamelCase__  =     self.image_processor(__lowerCamelCase				,     return_tensors=__lowerCamelCase				,     **__lowerCamelCase     )
     if text is not None:
      lowerCamelCase__  =     self.char_tokenizer(__lowerCamelCase				,     return_tensors=__lowerCamelCase				,     **__lowerCamelCase     )

     if text is None:
      return inputs
     elif images is None:
      return encodings
     else:
      lowerCamelCase__  =     encodings["input_ids"]
      return inputs

    def    a__				(					self     :     Any				,     __lowerCamelCase     :     Tuple     )							->							Tuple:

     '''simple docstring'''




     lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  =     sequences
     lowerCamelCase__  =     char_preds.size(0     )

     lowerCamelCase__  ,	lowerCamelCase__  =     self._decode_helper(__lowerCamelCase				,     "char"     )
     lowerCamelCase__  ,	lowerCamelCase__  =     self._decode_helper(__lowerCamelCase				,     "bpe"     )
     lowerCamelCase__  ,	lowerCamelCase__  =     self._decode_helper(__lowerCamelCase				,     "wp"     )

     lowerCamelCase__  =     []
     lowerCamelCase__  =     []
     for i in range(__lowerCamelCase     ):
      lowerCamelCase__  =     [char_scores[i], bpe_scores[i], wp_scores[i]]
      lowerCamelCase__  =     [char_strs[i], bpe_strs[i], wp_strs[i]]
      lowerCamelCase__  =     scores.index(max(__lowerCamelCase     )     )
      final_strs.append(strs[max_score_index]     )
      final_scores.append(scores[max_score_index]     )

     lowerCamelCase__  =     {}
     lowerCamelCase__  =     final_strs
     lowerCamelCase__  =     final_scores
     lowerCamelCase__  =     char_strs
     lowerCamelCase__  =     bpe_strs
     lowerCamelCase__  =     wp_strs
     return out

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     Any     )							->							Optional[int]:

     '''simple docstring'''




     if format == DecodeType.CHARACTER:
      lowerCamelCase__  =     self.char_decode
      lowerCamelCase__  =     1
      lowerCamelCase__  =     "[s]"
     elif format == DecodeType.BPE:
      lowerCamelCase__  =     self.bpe_decode
      lowerCamelCase__  =     2
      lowerCamelCase__  =     "#"
     elif format == DecodeType.WORDPIECE:
      lowerCamelCase__  =     self.wp_decode
      lowerCamelCase__  =     102
      lowerCamelCase__  =     "[SEP]"
     else:
      raise ValueError(f'''Format {format} is not supported.'''     )

     lowerCamelCase__  ,	lowerCamelCase__  =     [], []
     lowerCamelCase__  =     pred_logits.size(0     )
     lowerCamelCase__  =     pred_logits.size(1     )
     lowerCamelCase__  ,	lowerCamelCase__  =     pred_logits.topk(1				,     dim=-1				,     largest=__lowerCamelCase				,     sorted=__lowerCamelCase     )
     lowerCamelCase__  =     preds_index.view(-1				,     __lowerCamelCase     )[:, 1:]
     lowerCamelCase__  =     decoder(__lowerCamelCase     )
     lowerCamelCase__  ,	lowerCamelCase__  =     torch.nn.functional.softmax(__lowerCamelCase				,     dim=2     ).max(dim=2     )
     lowerCamelCase__  =     preds_max_prob[:, 1:]

     for index in range(__lowerCamelCase     ):
      lowerCamelCase__  =     preds_str[index].find(__lowerCamelCase     )
      lowerCamelCase__  =     preds_str[index][:pred_eos]
      lowerCamelCase__  =     preds_index[index].cpu().tolist()
      lowerCamelCase__  =     pred_index.index(__lowerCamelCase     ) if eos_token in pred_index else -1
      lowerCamelCase__  =     preds_max_prob[index][: pred_eos_index + 1]
      lowerCamelCase__  =     pred_max_prob.cumprod(dim=0     )[-1] if pred_max_prob.nelement() != 0 else 0.0
      dec_strs.append(__lowerCamelCase     )
      conf_scores.append(__lowerCamelCase     )

     return dec_strs, conf_scores

    def    a__				(					self     :     Any				,     __lowerCamelCase     :     Tuple     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  =     [seq.replace(" "				,     ""     ) for seq in self.char_tokenizer.batch_decode(__lowerCamelCase     )]
     return decode_strs

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     Tuple     )							->							Union[str, Any]:

     '''simple docstring'''




     return self.bpe_tokenizer.batch_decode(__lowerCamelCase     )




    def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     int     )							->							Optional[int]:

     '''simple docstring'''




     lowerCamelCase__  =     [seq.replace(" "				,     ""     ) for seq in self.wp_tokenizer.batch_decode(__lowerCamelCase     )]
     return decode_strs

